Covid19Japan.com
Covid19 Japanが独自に収集している陽性者単位のデータ（個票データ）。ソースとデータは全てGitHubにて公開されており、データはJSON形式。「レコード数 \(\neq\) 累計陽性者数」であることに注意。
Import
Covid19 JapanがGitHubで公開しているデータは前述のようにJSON形式であり、最新データはlatest.jsonファイルで示されている。このため、読み込む際はひと工夫必要。
個票データ（Patient Data）
陽性者単位の個票データ。
# path <- "https://raw.githubusercontent.com/reustle/covid19japan-data/master/docs/patient_data/"
#
# df <- path %>%
# paste0("latest.json") %>%
# readr::read_lines() %>%
# paste0(path, .) %>%
# jsonlite::fromJSON()
df <- "https://raw.githubusercontent.com/reustle/covid19japan-data/master/docs/patient_data/latest.json" %>%
jsonlite::fromJSON()
df
集計データ（Summary Data）
死亡者数や重症者数などの推移データはsummaryフォルダ内のJSON形式ファイルにまとめられている。読み込むと分かるがリスト型で、その中データフレームが含まれる形式である。
summaryフォルダの他にsummary_minフォルダというフォルダがあるが、summary_minフォルダ内のJSONファイルは単に改行を省略して小さくしたファイル。
# path <- "https://raw.githubusercontent.com/reustle/covid19japan-data/master/docs/summary/"
#
# df_s <- path %>%
# paste0("latest.json") %>%
# readr::read_lines() %>%
# paste0(path, .) %>%
# jsonlite::fromJSON()
df_s <- "https://raw.githubusercontent.com/reustle/covid19japan-data/master/docs/summary/latest.json" %>%
jsonlite::fromJSON()
df_s %>% summary()## Length Class Mode
## prefectures 27 data.frame list
## regions 12 data.frame list
## daily 37 data.frame list
## updated 1 -none- character
三つのデータフレームと一つのベクトル（更新日時）から構成されている。データフレームは上から順に都道府県別、地方別、日次となっているが、Lengthを見てわかるようにそれぞれに含まれる集計データが異なっている。
都道府県単位集計
更新日時（$updated）における都道府県単位での累積値。厚生労働省がオープンデータから除いている空港検疫・ダイヤモンドプリンセス・長崎クルーズ船・その他が含まれるので全51区分になっている。
df_s$prefectures陽性者・死亡者などの時系列集計データがネストされて格納されている。日付はネストされていないので、各項目に対するstartDateの項を参照すること。
| 項目 | 内容 | 備考 |
|---|---|---|
| dailyConfirmedCount | 陽性者数 | 単日 |
| dailyConfirmedStartDate | 陽性者数のカウント開始日 | 区分により開始日が異なる |
| dailyDeceasedCount | 死亡者数 | 単日 |
| dailyDeceasedStartDate | 死亡者数のカウント開始日 | 区分により開始日が異なる |
| dailyRecoveredCumulative | 快復者数 | 累計 |
| dailyRecoveredStartDate | 快復者数のカウント開始日 | 区分により開始日が異なる |
| dailyActive | 治療者数1 | 単日 |
| dailyActiveStartDate | 治療者数のカウント開始日 | 区分により開始日が異なる |
1 陽性者数から死亡者数と快復者数を引いた数値を治療者数としている
地方単位集計
更新日次時点における地方区分単位での累積値。陽性者の時系列集計データが都道府県単位データと同様にネストで格納されているが、死亡者・快復者・治療者のデータは含まれていない。
なお、時系列データの合計値と累積項の値が一致しない場合がある。
df_s$regionsdf_s$regions$confirmed[1]## [1] 188800df_s$regions$dailyConfirmedCount[[1]] %>% sum()## [1] 209346
日次集計
個票データを日次で集計したもの。日付を見れば分かる通り暗黙の欠落を含んでいる。
df_s$daily
更新日時
集計データの更新日時。
df_s$updated## [1] "2021-01-25T21:10:20+09:00"
Others
病床データ
新型コロナウイルス対策病床オープンデータのデータも用意しておく。
if (googlesheets4::gs4_has_token()) {
beds_by_pref <- "https://docs.google.com/spreadsheets/d/1u0Ul8TgJDqoZMnqFrILyXzTHvuHMht1El7wDZeVrpp8" %>%
googlesheets4::read_sheet() %>%
dplyr::arrange(dplyr::desc(`発表日`)) %>%
dplyr::distinct(`自治体名`, .keep_all = TRUE) %>%
dplyr::rename(pref = `自治体名`, beds = `新型コロナウイルス対策感染症病床数`,
date = `発表日`) %>%
dplyr::mutate(beds = as.integer(beds), date = lubridate::as_date(date))
beds_by_pref
}
対策ダッシュボード
NECソリューションイノベータによる都道府県単位の単日集計データ。治療に関する集計データが含まれている。ただし、項目によっては累積値（累計値）のものもある。
"https://covid-19.nec-solutioninnovators.com/download/japan_covid19.csv" %>%
readr::read_csv(guess_max = 12500) %>%
dplyr::select(date = `公表_年月日`, pref = `都道府県名`,
confirmed = `PCR検査陽性者`, pcr = `PCR検査実施人数`,
`入院治療等を要する者`, `うち重症`, `退院又は療養解除となった者の数`,
`確認中`) %>%
dplyr::mutate(date = lubridate::as_date(date)) %>%
dplyr::mutate_if(is.numeric, .funs = as.integer)
新型コロナ関連ニュース
新型コロナ関連のニュース
news <- "https://gist.githubusercontent.com/k-metrics/76fea197fa32466a2f99ff59f721b98a/raw/4c971a6cde2033e458525b793e832c4a87cbae2d/covid19_news.csv" %>%
readr::read_csv() %>%
dplyr::filter(area == "日本")
news
Summarize
最初に個票データの内容を確認する。これには要約に便利なskimrパッケージを用いる。
df %>%
skimr::skim()| Name | Piped data |
| Number of rows | 373753 |
| Number of columns | 17 |
| _______________________ | |
| Column type frequency: | |
| character | 15 |
| logical | 1 |
| numeric | 1 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| patientId | 0 | 1.00 | 1 | 16 | 0 | 368646 | 0 |
| dateAnnounced | 0 | 1.00 | 10 | 10 | 0 | 363 | 0 |
| gender | 263033 | 0.30 | 1 | 1 | 0 | 2 | 0 |
| detectedPrefecture | 0 | 1.00 | 3 | 15 | 0 | 49 | 0 |
| patientStatus | 366388 | 0.02 | 8 | 23 | 0 | 8 | 0 |
| mhlwPatientNumber | 373304 | 0.00 | 1 | 11 | 0 | 434 | 0 |
| prefecturePatientNumber | 256308 | 0.31 | 5 | 20 | 0 | 117436 | 0 |
| residence | 271123 | 0.27 | 1 | 38 | 0 | 1429 | 0 |
| relatedPatients | 361166 | 0.03 | 2 | 259 | 0 | 7470 | 0 |
| knownCluster | 371219 | 0.01 | 3 | 88 | 0 | 235 | 0 |
| detectedCityTown | 344720 | 0.08 | 2 | 22 | 0 | 667 | 0 |
| cityPrefectureNumber | 345021 | 0.08 | 1 | 34 | 0 | 28723 | 2 |
| deceasedDate | 368549 | 0.01 | 10 | 10 | 0 | 313 | 0 |
| deceasedReportedDate | 372468 | 0.00 | 10 | 62 | 0 | 209 | 0 |
| deathSourceURL | 372611 | 0.00 | 14 | 123 | 0 | 659 | 0 |
Variable type: logical
| skim_variable | n_missing | complete_rate | mean | count |
|---|---|---|---|---|
| confirmedPatient | 0 | 1 | 0.99 | TRU: 368645, FAL: 5108 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| ageBracket | 263146 | 0.3 | 38.01 | 20.69 | 0 | 20 | 30 | 50 | 100 | ▇▇▅▂▁ |
元がJSON形式なので、読み込んだ直後は殆どの変量（フィーチャー）が文字型になっていることが分かる。また、意外と欠損が多いことも分かる。
Tidy & Transform
各変量（フィーチャー）を適切な形式に変換し、地域区分でも分析できるように都道府県データと結合することで、ベースとなるデータセットを作成する。なお、都道府県以外で報告されたレコードを除いている。
x <- df %>%
dplyr::select(patientId, date = dateAnnounced, gender,
pref = detectedPrefecture, patientStatus, knownCluster,
confirmedPatient,
# charterFlightPassenger, cruisePassengerDisembarked,
ageBracket,
deceasedDate, deceasedReportedDate) %>%
dplyr::filter(confirmedPatient == TRUE) %>%
dplyr::mutate(date = lubridate::as_date(date),
gender = forcats::as_factor(gender),
patientStatus = forcats::as_factor(patientStatus),
cluster = dplyr::if_else(!is.na(knownCluster), TRUE, FALSE),
ageBracket = forcats::as_factor(ageBracket),
deceasedDate = lubridate::as_date(deceasedDate),
deceasedReportedDate = lubridate::as_date(deceasedReportedDate)) %>%
dplyr::left_join(prefs, by = c("pref" = "pref")) %>%
dplyr::select(-`推計人口`, -pref) %>%
dplyr::rename(pref = `都道府県`, region = `八地方区分`) %>%
tidyr::drop_na(pref)
x変換結果を要約してみると
x %>%
skimr::skim()| Name | Piped data |
| Number of rows | 366503 |
| Number of columns | 16 |
| _______________________ | |
| Column type frequency: | |
| character | 2 |
| Date | 3 |
| factor | 9 |
| logical | 2 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| patientId | 0 | 1.00 | 2 | 16 | 0 | 366503 | 0 |
| knownCluster | 364019 | 0.01 | 3 | 88 | 0 | 232 | 0 |
Variable type: Date
| skim_variable | n_missing | complete_rate | min | max | median | n_unique |
|---|---|---|---|---|---|---|
| date | 0 | 1 | 2020-01-15 | 2021-01-25 | 2020-12-16 | 360 |
| deceasedDate | 366124 | 0 | 2020-02-13 | 2020-11-19 | 2020-05-08 | 150 |
| deceasedReportedDate | 366174 | 0 | 2020-02-13 | 2020-10-17 | 2020-05-16 | 130 |
Variable type: factor
| skim_variable | n_missing | complete_rate | ordered | n_unique | top_counts |
|---|---|---|---|---|---|
| gender | 257388 | 0.30 | FALSE | 2 | M: 61012, F: 48103 |
| patientStatus | 363992 | 0.01 | FALSE | 8 | Hos: 1246, Dec: 371, Hom: 315, Dis: 276 |
| ageBracket | 257473 | 0.30 | FALSE | 12 | 20: 29297, 30: 18917, 40: 15988, 50: 14066 |
| pcode | 0 | 1.00 | FALSE | 47 | 13: 94576, 27: 41728, 14: 38429, 11: 23628 |
| pref | 0 | 1.00 | FALSE | 47 | 東京都: 94576, 大阪府: 41728, 神奈川: 38429, 埼玉県: 23628 |
| region | 0 | 1.00 | FALSE | 8 | 関東地: 188800, 近畿地: 72944, 中部地: 37817, 九州地: 32196 |
| 広域圏 | 23822 | 0.94 | FALSE | 8 | 首都圏: 189688, 近畿圏: 70921, 中部圏: 35342, 九州圏: 25123 |
| 通俗的区分 | 0 | 1.00 | FALSE | 11 | 関東: 188800, 関西: 70921, 東海: 33149, 九州: 25123 |
| fct_pref | 0 | 1.00 | FALSE | 47 | Tok: 94576, Osa: 41728, Kan: 38429, Sai: 23628 |
Variable type: logical
| skim_variable | n_missing | complete_rate | mean | count |
|---|---|---|---|---|
| confirmedPatient | 0 | 1 | 1.00 | TRU: 366503 |
| cluster | 0 | 1 | 0.01 | FAL: 364019, TRU: 2484 |
文字型を因子型に変換するだけでも大まかな傾向が見えるようになる。例えば
- 年齢別で見ると20代、30代、年齢不明（恐らく非回答）、40代の順に多い
- 都道府県別では東京、大阪、神奈川、愛知の順と人口にほぼ比例
- 地方区分で見ると関東、近畿、九州、中部となっており九州地方が以外と多い
ことが読める。
patientStatusは以下の通りで、ほぼ更新されていないのと思われる。死者数などの推移を見る場合は集計データを使った方がいいことが分かる。
x %>%
dplyr::group_by(patientStatus) %>%
dplyr::summarise(n = n()) %>%
dplyr::ungroup() %>%
dplyr::mutate(Japanese = c("回復", "入院中", "退院済", "死亡", "詳細不明",
"重症", "自宅療養", "ホテル療養", NA))
Data Wrangling
陽性者の集計
最初に陽性者をキーに集計する。
全国集計
全国の累計陽性者数と推計人口[千人]、ならびに、人口千人あたりの累計陽性者数。
r_by_all <- x %>%
dplyr::filter(!is.na(pref)) %>%
dplyr::summarise(n = n()) %>%
dplyr::bind_cols(prefs %>% dplyr::summarise(population = sum(`推計人口`))) %>%
dplyr::mutate(rate = round(n / population, 2))
r_by_all %>%
dplyr::rename(`累計陽性者数[人]` = n, `推計人口[千人]` = population,
`人口千人あたりの累計陽性者数` = rate)
地方別集計
次に地方別の累計陽性者数と推計人口[千人]、ならびに、人口千人あたりの累計陽性者数。
region <- prefs %>%
dplyr::group_by(`八地方区分`) %>%
dplyr::summarise(population = sum(`推計人口`)) %>%
dplyr::rename(region = `八地方区分`)
r_by_region <- x %>%
dplyr::group_by(region) %>%
dplyr::summarise(n = n()) %>%
tidyr::drop_na() %>%
dplyr::left_join(region, by = c("region" = "region")) %>%
dplyr::select(region, n, population) %>%
dplyr::mutate(rate = round(n / population, 2))
r_by_region %>%
dplyr::rename(`地方` = region,
`累計陽性者数[人]` = n, `推計人口[千人]` = population,
`人口千人あたりの累計陽性者数` = rate)
上表を可視化する。グレーの破線は切片ゼロで傾きが全国の人口千人あたりの累計陽性者数（2.9）。
r_by_region %>%
dplyr::rename(key = region) %>%
ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) +
ggplot2::geom_abline(slope = r_by_all$rate, intercept = 0,
colour = "gray", linetype = "dashed") +
ggplot2::geom_point(ggplot2::aes(colour = key)) +
ggrepel::geom_text_repel(ggplot2::aes(label = key, colour = key)) +
ggplot2::theme(legend.position = 'none') +
ggplot2::labs(title = paste0("推計人口と累計陽性者数 @", datetime),
subtitle = subtitle, caption = caption,
x = "推計人口[千人]", y = "累計陽性者数[人]")
都道府県別集計
同様に都道府県別の累計陽性者数と推計人口[千人]、ならびに、人口千人あたりの累計陽性者数。任意の列でソートできるようにしてある。
r_by_pref <- x %>%
dplyr::group_by(pref) %>%
dplyr::summarise(n = n()) %>%
tidyr::drop_na() %>%
dplyr::left_join(prefs, by = c("pref" = "都道府県")) %>%
dplyr::select(pref, n, population = `推計人口`) %>%
dplyr::mutate(rate = round(n / population, 2))
r_by_pref %>%
dplyr::rename(`都道府県` = pref,
`累計陽性者数[人]` = n, `推計人口[千人]` = population,
`人口千人あたりの累計陽性者数` = rate) %>%
tibble::rowid_to_column("No") %>%
DT::datatable()
上表を可視化する。グレーの破線は切片ゼロで傾きが全国の人口千人あたりの累計陽性者数（2.9）。
r_by_pref %>%
dplyr::rename(key = pref) %>%
ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) +
ggplot2::geom_abline(slope = r_by_all$rate, intercept = 0,
colour = "gray", linetype = "dashed") +
ggplot2::geom_point(ggplot2::aes(colour = key)) +
ggrepel::geom_text_repel(ggplot2::aes(label = key, colour = key)) +
ggplot2::theme(legend.position = 'none') +
ggplot2::labs(title = paste0("推計人口と累計陽性者数 @", datetime),
subtitle = subtitle, caption = caption,
x = "推計人口[千人]", y = "累計陽性者数[人]")
推計人口が550万人未満の都道府県のみ抽出する。グレーの破線は上図と同様。
r_by_pref %>%
dplyr::filter(population < 5500) %>%
dplyr::rename(key = pref) %>%
ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) +
ggplot2::geom_abline(slope = r_by_all$rate, intercept = 0,
colour = "gray", linetype = "dashed") +
ggplot2::geom_point(ggplot2::aes(colour = key)) +
ggrepel::geom_text_repel(ggplot2::aes(label = key, colour = key)) +
ggplot2::theme(legend.position = 'none') +
ggplot2::labs(title = paste0("推計人口と累計陽性者数 @", datetime),
subtitle = subtitle, caption = caption,
x = "推計人口[千人]", y = "累計陽性者数[人]")
クラスタ比率
全国のクラスタ比率
x %>%
dplyr::filter(!is.na(pref)) %>%
dplyr::group_by(cluster) %>%
dplyr::summarise(n = n()) %>%
tidyr::pivot_wider(names_from = cluster, values_from = n) %>%
dplyr::mutate(ratio = (`TRUE` / (`TRUE` + `FALSE`) * 100) %>% round(1)) %>%
dplyr::rename(`非クラスタ感染者[人]` = `FALSE`, `クラスタ感染者[人]` = `TRUE`,
`クラスタ比率[%]` = ratio)地方別クラスタ比率
地方別の累計陽性者数の内、クラスタ感染と判定された人数の割合を求める。
x %>%
dplyr::group_by(region, cluster) %>%
dplyr::summarise(n = n()) %>%
tidyr::drop_na() %>%
tidyr::pivot_wider(names_from = cluster, values_from = n) %>%
dplyr::mutate(ratio = (`TRUE` / (`TRUE` + `FALSE`) * 100) %>% round(1)) %>%
dplyr::rename(`地方` = region,
`非クラスタ感染者[人]` = `FALSE`, `クラスタ感染者[人]` = `TRUE`,
`クラスタ比率[%]` = ratio)
都道府県別クラスタ比率
同様に都道府県別のクラスタ比率。任意の列でソートできるようにしてある。
x %>%
dplyr::group_by(pref, cluster) %>%
dplyr::summarise(n = n()) %>%
tidyr::drop_na() %>%
tidyr::pivot_wider(names_from = cluster, values_from = n) %>%
dplyr::mutate(ratio = (`TRUE` / (`TRUE` + `FALSE`) * 100) %>% round(1)) %>%
tidyr::replace_na(list(`TRUE` = 0L, ratio = 0.0)) %>%
dplyr::rename(`都道府県` = pref,
`非クラスタ感染者[人]` = `FALSE`, `クラスタ感染者[人]` = `TRUE`,
`クラスタ比率[%]` = ratio) %>%
tibble::rowid_to_column(var = "No") %>%
DT::datatable()
陽性者の日次集計
全国日次集計
全国の日次単位の陽性者数、前日差、累計、移動平均を求める。
x_by_all <- x %>%
dplyr::group_by(date) %>%
dplyr::summarise(n = n()) %>%
tidyr::complete(date = seq.Date(from = min(date), to = max(date), by = "day"),
fill = list(n = 0L)) %>%
dplyr::mutate(diff = lagdiff(n), cum = cumsum(n), ma7 = ma7(n), ma28 = ma28(n))
x_by_all %>%
dplyr::select(`発表日` = date, `陽性者数` = n, `前日差` = diff,
`累計陽性者数` = cum, `移動平均（7日）` = ma7)
上表を可視化する。
# 祝日ファイルは以下をダウンロードしておく（SSLがエラーになるので自動処理できない）
# "https://www8.cao.go.jp/chosei/shukujitsu/syukujitsu.csv"
# 祝日判定関数
source("https://raw.githubusercontent.com/logics-of-blue/website/master/010_forecast/20190714_R%E8%A8%80%E8%AA%9E%E3%81%AB%E3%81%8A%E3%81%91%E3%82%8B%E6%97%A5%E6%9C%AC%E3%81%AE%E7%A5%9D%E6%97%A5%E5%88%A4%E5%AE%9A/jholiday.R", encoding="utf-8")
sec_scale <- 100
emergency <- news %>%
dplyr::filter(category == "緊急事態")
goto <- news %>%
dplyr::filter(category == "GoTo")
x_by_all %>%
dplyr::mutate(hday = is.jholiday(target_date = date,
holiday_source = "./Covid19/syukujitsu.csv"),
wday = lubridate::wday(date, week_start = 1),
wday = dplyr::if_else(wday > 5, TRUE, FALSE),
wday = dplyr::if_else(hday | wday, 3000L, NA_integer_)) %>%
ggplot2::ggplot(ggplot2::aes(x = date)) +
ggplot2::geom_bar(ggplot2::aes(y = wday), stat = "identity", width = 1.0,
fill = "red", alpha = 0.1) +
ggplot2::geom_vline(ggplot2::aes(xintercept = date), data = emergency,
colour = "dark blue", linetype = "dashed", size = 0.15) +
ggplot2::geom_vline(ggplot2::aes(xintercept = date), data = goto,
colour = "magenta", linetype = "dashed", size = 0.25) +
ggplot2::geom_bar(ggplot2::aes(y = n), stat = "identity", width = 1.0,
fill = "dark gray", alpha = 1.0) +
ggplot2::geom_line(ggplot2::aes(y = ma7), linetype = "dashed",
colour = "dark green", size = 0.5) +
ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale),
colour = "dark green", size = 1.0) +
ggplot2::labs(title = paste0("【全国】陽性者数の推移（単日） @", datetime),
subtitle = subtitle, caption = caption,
x = "", y = "") +
ggrepel::geom_label_repel(ggplot2::aes(x = date, y = 2500, label = news),
size = 2.0, data = emergency) +
ggrepel::geom_label_repel(ggplot2::aes(x = date, y = 2250, label = news),
size = 2.5, data = goto, colour = "magenta") +
ggplot2::scale_y_continuous(
name = "陽性者数・移動平均（破線）",
sec.axis = ggplot2::sec_axis(~ . * sec_scale,
name = "累積陽性者数（折線）")
)
x_by_all %>%
ggplot2::ggplot(ggplot2::aes(x = date)) +
ggplot2::geom_line(ggplot2::aes(y = diff), colour = "dark green", alpha = 0.5) +
ggplot2::labs(title = paste0("【全国】陽性者数の前日差 @", datetime),
subtitle = subtitle, caption = caption,
x = "", y = "前日差")
地方別日次集計
同様に地方別の日次単位の陽性者数、前日差、累計、移動平均を求める。
x_by_region <- x %>%
dplyr::group_by(date, region) %>%
dplyr::summarise(n = n()) %>%
dplyr::ungroup() %>%
tidyr::pivot_wider(names_from = region, values_from = n, values_fill = 0L) %>%
tidyr::complete(date = seq.Date(from = min(date), to = max(date), by = "day")) %>%
tidyr::pivot_longer(cols = -date, names_to = "region", values_to = "n") %>%
tidyr::replace_na(replace = list(n = 0L)) %>%
dplyr::group_by(region) %>%
tidyr::nest() %>%
dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
cum = purrr::map(data, ~ cumsum(.$n)),
ma7 = purrr::map(data, ~ ma7(.$n)),
ma28 = purrr::map(data, ~ ma28(.$n))) %>%
tidyr::unnest() %>%
dplyr::left_join(prefs %>% dplyr::distinct(`八地方区分`), .,
by = c("八地方区分" = "region")) %>%
dplyr::mutate(region = forcats::fct_inorder(`八地方区分`)) %>%
dplyr::arrange(date)
x_by_region %>%
dplyr::filter(date == max(date)) %>%
dplyr::mutate(ma7 = round(ma7, 1)) %>%
dplyr::select(`地方` = region,
`発表日` = date, `陽性者数` = n, `前日差` = diff,
`陽性者累計` = cum, `移動平均（7日）` = ma7)x_by_region %>%
dplyr::select(`地方` = region,
`発表日` = date, `陽性者数` = n, `前日差` = diff,
`陽性者累計` = cum, `移動平均（7日）` = ma7)
上表を可視化する。
x_by_region %>%
ggplot2::ggplot(ggplot2::aes(x = date, y = n)) +
ggplot2::geom_bar(ggplot2::aes(y = n, fill = region), stat = "identity",
width = 1.0, alpha = 0.5) +
ggplot2::labs(title = paste0("【地方別】陽性者数の推移（単日） @", datetime),
subtitle = subtitle, caption = caption,
x = "", y = "陽性者数") 
x_by_region %>%
ggplot2::ggplot(ggplot2::aes(x = date, y = ma7, colour = region)) +
ggplot2::geom_line(size = 1) +
ggplot2::theme(legend.position = 'none') +
ggplot2::labs(title = paste0("【地方別】移動平均（7日） @", datetime),
subtitle = subtitle, caption = caption,
x = "", y = "陽性者数") +
ggrepel::geom_text_repel(ggplot2::aes(label = region),
data = subset(x_by_region, date == max(date)),
nudge_x = 30, segment.alpha = 0.5, size = 4) +
ggplot2::lims(x = c(min(x_by_region$date),
max(x_by_region$date) + 45))
x_by_region %>%
ggplot2::ggplot(ggplot2::aes(x = date, y = cum, colour = region)) +
ggplot2::geom_line(size = 1) +
ggplot2::theme(legend.position = 'none') +
ggplot2::labs(title = paste0("【地方別】累計陽性者数 @", datetime),
subtitle = subtitle, caption = caption,
x = "", y = "累計陽性者数") +
ggrepel::geom_text_repel(ggplot2::aes(label = region),
data = subset(x_by_region, date == max(date)),
nudge_x = 30, segment.alpha = 0.5, size = 4) +
ggplot2::lims(x = c(min(x_by_region$date),
max(x_by_region$date) + 45))
地方単位で可視化。
sec_scale <- 20
ncol <- 2
x_by_region %>%
dplyr::rename(key = region) %>%
ggplot2::ggplot(ggplot2::aes(x = date)) +
ggplot2::geom_bar(ggplot2::aes(y = n, fill = key), stat = "identity",
alpha = 0.5, width = 1.0) +
ggplot2::geom_line(ggplot2::aes(y = ma7, colour = key),
linetype = "dotted", size = 0.5) +
ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = key)) +
ggplot2::facet_wrap(~ key, ncol = ncol) +
ggplot2::theme(legend.position = 'none') +
ggplot2::labs(title = paste0("Fixed scale @", datetime),
subtitle = subtitle, caption = caption,
x = "", y = "") +
ggplot2::scale_y_continuous(
name = "陽性者数・移動平均（点線）",
sec.axis = ggplot2::sec_axis(~ . * sec_scale,
name = "陽性者累計（実線）")
)
傾向が見えるように縦軸をフリースケールとする。
sec_scale <- 20
ncol <- 2
x_by_region %>%
dplyr::rename(key = region) %>%
ggplot2::ggplot(ggplot2::aes(x = date)) +
ggplot2::geom_bar(ggplot2::aes(y = n, fill = key), stat = "identity",
alpha = 0.5, width = 1.0) +
ggplot2::geom_line(ggplot2::aes(y = ma7, colour = key),
linetype = "dashed", size = 0.5) +
ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = key)) +
ggplot2::facet_wrap(~ key, ncol = ncol, scales = "free_y") +
ggplot2::theme(legend.position = 'none') +
ggplot2::labs(title = paste0("Free Y scale @", datetime),
subtitle = subtitle, caption = caption,
x = "", y = "") +
ggplot2::scale_y_continuous(
name = "陽性者数・移動平均（破線）",
sec.axis = ggplot2::sec_axis(~ . * sec_scale,
name = "陽性者累計（実線）")
)
都道府県別日次集計
同様に都道府県別の日次単位の陽性者数、前日差、累計、移動平均を求める。
x_by_pref <- x %>%
dplyr::group_by(date, pref) %>%
dplyr::summarise(n = n()) %>%
dplyr::ungroup() %>%
tidyr::pivot_wider(names_from = pref, values_from = n, values_fill = 0L) %>%
tidyr::complete(date = seq.Date(from = min(date), to = max(date), by = "day")) %>%
tidyr::pivot_longer(cols = -date, names_to = "pref", values_to = "n") %>%
tidyr::replace_na(replace = list(n = 0L)) %>%
dplyr::group_by(pref) %>%
tidyr::nest() %>%
dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
cum = purrr::map(data, ~ cumsum(.$n)),
ma7 = purrr::map(data, ~ ma7(.$n)),
ma28 = purrr::map(data, ~ ma28(.$n))) %>%
tidyr::unnest() %>%
dplyr::left_join(prefs, ., by = c("都道府県" = "pref")) %>%
dplyr::mutate(pref = forcats::fct_inorder(`都道府県`)) %>%
dplyr::arrange(date)
x_by_pref %>%
dplyr::filter(date == max(date)) %>%
dplyr::mutate(ma7 = round(ma7, 1)) %>%
dplyr::select(`都道府県` = pref,
`発表日` = date, `陽性者数` = n, `前日差` = diff,
`陽性者累計` = cum, `移動平均（7日）` = ma7) %>%
DT::datatable()x_by_pref %>%
dplyr::select(`都道府県` = pref,
`発表日` = date, `陽性者数` = n, `前日差` = diff,
`陽性者累計` = cum, `移動平均（7日）` = ma7)
上表を可視化する。
sec_scale <- 100
ncol <- 5
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")
x_by_pref %>%
dplyr::rename(key = pref) %>%
ggplot2::ggplot(ggplot2::aes(x = date)) +
ggplot2::geom_bar(ggplot2::aes(y = n, fill = key), stat = "identity",
alpha = 0.25, width = 1.0) +
ggplot2::geom_line(ggplot2::aes(y = ma7, colour = key),
linetype = "solid", size = 0.25) +
ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = key)) +
ggplot2::facet_wrap(~ key, ncol = ncol) +
ggplot2::theme(legend.position = 'none') +
ggplot2::labs(title = paste0("Fixed scale @", datetime),
subtitle = subtitle, caption = caption,
x = "", y = "") +
ggplot2::scale_y_continuous(
name = "陽性者数・移動平均（細線）",
sec.axis = ggplot2::sec_axis(~ . * sec_scale,
name = "累計陽性者数（折線）")
)
傾向が見えるように縦軸をフリースケールとする。
x_by_pref %>%
dplyr::rename(key = pref) %>%
ggplot2::ggplot(ggplot2::aes(x = date)) +
ggplot2::geom_bar(ggplot2::aes(y = n, fill = key), stat = "identity",
alpha = 0.35, width = 1.0) +
ggplot2::geom_line(ggplot2::aes(y = ma7, colour = key),
linetype = "solid", size = 0.25) +
ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = key)) +
ggplot2::facet_wrap(~ key, ncol = ncol, scales = "free_y") +
ggplot2::theme(legend.position = 'none') +
ggplot2::labs(title = paste0("Free Y scale @", datetime),
subtitle = subtitle, caption = caption,
x = "", y = "") +
ggplot2::scale_y_continuous(
name = "陽性者数・移動平均（細線）",
sec.axis = ggplot2::sec_axis(~ . * sec_scale,
name = "累計陽性者数（折線）")
)
死亡者の日次集計
厚生労働省のデータと乖離がある。
都道府県別
都道府県別の日次単位の死亡者数、前日差、累計、移動平均（7日）を求める。
start <- df_s$prefectures %>%
dplyr::select(pref = name, date = dailyDeceasedStartDate) %>%
dplyr::left_join(prefs, by = c("pref" = "pref")) %>%
dplyr::arrange(pcode) %>%
tidyr::drop_na(pcode) %>%
dplyr::select(date, pref = `都道府県`) %>%
dplyr::distinct(date) %>%
.$date %>% lubridate::as_date()
d_by_prefs <- df_s$prefectures %>%
dplyr::select(deceased = dailyDeceasedCount, pref = name) %>%
dplyr::left_join(prefs, by = c("pref" = "pref")) %>%
tidyr::drop_na(pcode) %>%
dplyr::select(pref = `都道府県`, deceased) %>%
tidyr::unnest(deceased) %>%
tidyr::pivot_wider(names_from = pref, values_from = deceased) %>%
tidyr::unnest() %>%
dplyr::mutate(date = seq.Date(from = start, to = start + nrow(.) - 1,
by = "day")) %>%
dplyr::select(date, dplyr::everything()) %>%
tidyr::pivot_longer(col = -date, names_to = "pref", values_to = "n") %>%
dplyr::group_by(pref) %>%
tidyr::nest() %>%
dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
cum = purrr::map(data, ~ cumsum(.$n)),
ma7 = purrr::map(data, ~ ma7(.$n))) %>%
tidyr::unnest() %>%
dplyr::left_join(prefs, ., by = c("都道府県" = "pref")) %>%
dplyr::mutate(pref = forcats::fct_inorder(`都道府県`)) %>%
dplyr::select(date, pref, n, diff, cum, ma7) %>%
dplyr::arrange(date)
d_by_prefssec_scale <- 100
ncol <- 5
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")
d_by_prefs %>%
dplyr::rename(key = pref) %>%
ggplot2::ggplot(ggplot2::aes(x = date)) +
ggplot2::geom_bar(ggplot2::aes(y = n, fill = key), stat = "identity",
alpha = 0.25, width = 1.0) +
ggplot2::geom_line(ggplot2::aes(y = ma7, colour = key),
linetype = "solid", size = 0.25) +
ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = key)) +
ggplot2::facet_wrap(~ key, ncol = ncol) +
ggplot2::theme(legend.position = 'none') +
ggplot2::labs(title = paste0("Fixed scale @", datetime),
subtitle = subtitle, caption = caption,
x = "", y = "") +
ggplot2::scale_y_continuous(
name = "陽性者数・移動平均（細線）",
sec.axis = ggplot2::sec_axis(~ . * sec_scale,
name = "累計陽性者数（折線）")
)
地方別
集計データ$regionsには死亡者数の日次データが存在しないため$prefecturesのデータから計算する。
d_by_region <- d_by_prefs %>%
dplyr::select(date, pref = pref, n) %>%
dplyr::left_join(prefs, by = c("pref" = "都道府県")) %>%
tidyr::drop_na(pcode) %>%
dplyr::group_by(date, `八地方区分`) %>%
dplyr::summarise(n = sum(n)) %>%
dplyr::ungroup() %>%
dplyr::rename(region = `八地方区分`) %>%
dplyr::group_by(region) %>%
tidyr::nest() %>%
dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
cum = purrr::map(data, ~ cumsum(.$n)),
ma7 = purrr::map(data, ~ ma7(.$n))) %>%
tidyr::unnest() %>%
dplyr::arrange(date)
d_by_region
陽性者比率と死亡者比率
rpd_by_all <- d_by_region %>%
dplyr::group_by(region) %>%
dplyr::summarise(d = sum(n)) %>%
dplyr::left_join(r_by_region, ., by = c("region")) %>%
dplyr::select(region, positive = n, deceased = d, population) %>%
dplyr::select(-region) %>%
dplyr::summarise_all(sum) %>%
dplyr::mutate(p_rate = round(positive / population, 2),
d_rate = round(deceased / positive, 2))
rpd_by_all %>%
dplyr::rename(`陽性者数` = positive, `死亡者数` = deceased,
`推計人口` = population, `人口千人あたりの陽性者比率` = p_rate,
`陽性者に対する死亡者比率` = d_rate)
rpd_by_region <- d_by_region %>%
dplyr::group_by(region) %>%
dplyr::summarise(d = sum(n)) %>%
dplyr::left_join(r_by_region, ., by = c("region")) %>%
dplyr::select(region, positive = n, deceased = d, population, p_rate = rate) %>%
dplyr::mutate(d_rate = round(deceased / positive, 2))
rpd_by_region %>%
dplyr::rename(`陽性者数` = positive, `死亡者数` = deceased,
`推計人口` = population, `人口千人あたりの陽性者比率` = p_rate,
`陽性者に対する死亡者比率` = d_rate)
rpd_by_prefs <- d_by_prefs %>%
dplyr::group_by(pref) %>%
dplyr::summarise(d = sum(n)) %>%
dplyr::left_join(r_by_pref, ., by = "pref") %>%
dplyr::select(pref, positive = n, deceased = d, population, p_rate = rate) %>%
dplyr::mutate(d_rate = round(deceased / positive, 2))
rpd_by_prefs %>%
dplyr::rename(`陽性者数` = positive, `死亡者数` = deceased,
`推計人口` = population, `人口千人あたりの陽性者比率` = p_rate,
`陽性者に対する死亡者比率` = d_rate)
全国日次集計
都道府県別のデータから全国の日次集計を求める。
d_by_all <- d_by_prefs %>%
dplyr::group_by(date) %>%
dplyr::summarise(n = sum(n)) %>%
dplyr::ungroup() %>%
dplyr::mutate(diff = lagdiff(n), cum = cumsum(n), ma7 = ma7(n))
d_by_allsec_scale <- 50
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")
d_by_all %>%
ggplot2::ggplot(ggplot2::aes(x = date)) +
ggplot2::geom_bar(ggplot2::aes(y = n), fill = "dark gray", stat = "identity",
alpha = 0.25, width = 1.0) +
ggplot2::geom_line(ggplot2::aes(y = ma7), colour = "dark green",
linetype = "dashed", size = 0.25) +
ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale), colour = "dark green") +
ggplot2::theme(legend.position = 'none') +
ggplot2::labs(title = paste0("Fixed scale @", datetime),
subtitle = subtitle, caption = caption,
x = "", y = "") +
ggplot2::scale_y_continuous(
name = "死亡者数・同移動平均（破線）",
sec.axis = ggplot2::sec_axis(~ . * sec_scale,
name = "累計死亡者数（実線）")
)
年代別
x_by_age <- x %>%
dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5"))) %>%
dplyr::mutate(ageBracket = forcats::fct_relevel(ageBracket,
"100", "90", "80", "70", "60",
"50", "40", "30", "20", "10",
"0-9"))
x_by_agex %>%
dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5"))) %>%
dplyr::group_by(ageBracket) %>%
dplyr::summarise(n = n()) %>%
dplyr::ungroup() %>%
dplyr::mutate(rate = round((n / sum(n) * 100), 1))
x %>%
dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5"))) %>%
dplyr::group_by(region, ageBracket) %>%
dplyr::summarise(n = n()) %>%
tidyr::pivot_wider(names_from = ageBracket, values_from = n)x %>%
dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5"))) %>%
dplyr::mutate(ageBracket = forcats::fct_relevel(ageBracket,
"100", "90", "80", "70", "60",
"50", "40", "30", "20", "10",
"0")) %>%
dplyr::group_by(region, ageBracket) %>%
dplyr::summarise(n = n()) %>%
dplyr::mutate(region = forcats::fct_rev(region)) %>%
ggplot2::ggplot(ggplot2::aes(x = region)) +
ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity",
position = "fill") +
ggplot2::scale_fill_brewer(palette = "Set3") +
ggplot2::scale_y_continuous(labels = scales::percent) +
ggplot2::coord_flip() +
ggplot2::labs(title = paste0("年代構成比 @", datetime),
subtitle = subtitle, caption = caption,
x = "", y = "")
x %>%
dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5"))) %>%
dplyr::mutate(ageBracket = forcats::fct_relevel(ageBracket,
"100", "90", "80", "70", "60",
"50", "40", "30", "20", "10",
"0")) %>%
dplyr::filter(ageBracket != "Unknown") %>%
dplyr::group_by(region, ageBracket) %>%
dplyr::summarise(n = n()) %>%
dplyr::mutate(region = forcats::fct_rev(region)) %>%
ggplot2::ggplot(ggplot2::aes(x = region)) +
ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity",
position = "fill") +
ggplot2::scale_fill_brewer(palette = "Set3") +
ggplot2::scale_y_continuous(labels = scales::percent) +
ggplot2::coord_flip() +
ggplot2::labs(title = paste0("年齢不明者をのぞく年代構成比 @", datetime),
subtitle = subtitle, caption = caption,
x = "", y = "")
x %>%
dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5"))) %>%
dplyr::mutate(ageBracket = forcats::fct_relevel(ageBracket,
"100", "90", "80", "70", "60",
"50", "40", "30", "20", "10",
"0")) %>%
dplyr::group_by(region, ageBracket, cluster) %>%
dplyr::summarise(n = n()) %>%
dplyr::mutate(region = forcats::fct_rev(region)) %>%
ggplot2::ggplot(ggplot2::aes(x = region)) +
ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity",
position = "fill") +
ggplot2::facet_grid(~ cluster) +
ggplot2::scale_fill_brewer(palette = "Set3") +
ggplot2::scale_y_continuous(labels = scales::percent) +
ggplot2::coord_flip() +
ggplot2::labs(title = paste0("非クラスタ/クラスタでの年代構成比 @", datetime),
subtitle = subtitle, caption = caption,
x = "", y = "")
x %>%
dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5"))) %>%
dplyr::mutate(ageBracket = forcats::fct_relevel(ageBracket,
"100", "90", "80", "70", "60",
"50", "40", "30", "20", "10",
"0")) %>%
dplyr::filter(ageBracket != "Unknown") %>%
dplyr::group_by(region, ageBracket, cluster) %>%
dplyr::summarise(n = n()) %>%
dplyr::mutate(region = forcats::fct_rev(region)) %>%
ggplot2::ggplot(ggplot2::aes(x = region)) +
ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity",
position = "fill") +
ggplot2::facet_grid(~ cluster) +
ggplot2::scale_fill_brewer(palette = "Set3") +
ggplot2::scale_y_continuous(labels = scales::percent) +
ggplot2::coord_flip() +
ggplot2::labs(title = paste0("年齢不明者をのぞく非クラスタ/クラスタでの年代構成比 @", datetime),
subtitle = subtitle, caption = caption,
x = "", y = "")
都道府県別
g_age_by_pref <- x %>%
dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
ageBracket = forcats::fct_collapse(ageBracket, `0-9` = c("0", "5"))) %>%
dplyr::mutate(ageBracket = forcats::fct_relevel(ageBracket,
"100", "90", "80", "70", "60",
"50", "40", "30", "20", "10",
"0-9")) %>%
dplyr::group_by(pref, ageBracket, cluster) %>%
# dplyr::group_by(pref, ageBracket) %>%
dplyr::summarise(n = n()) %>%
dplyr::mutate(pref = forcats::fct_rev(pref)) %>%
ggplot2::ggplot(ggplot2::aes(x = pref)) +
ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity",
position = "fill") +
# ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity") +
ggplot2::scale_fill_brewer(palette = "Set3") +
ggplot2::scale_y_continuous(labels = scales::percent) +
ggplot2::coord_flip() +
ggplot2::geom_hline(yintercept = c(0.25, 0.5, 0.75), size = 0.35,
colour = "dark gray") +
ggplot2::labs(title = paste0("年代構成比 @", datetime),
subtitle = subtitle, caption = caption,
x = "", y = "")
g_age_by_pref
g_age_by_pref +
ggplot2::facet_grid(~ cluster)
g_age_by_pref_wo <- x %>%
dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
ageBracket = forcats::fct_collapse(ageBracket, `0-9` = c("0", "5"))) %>%
dplyr::mutate(ageBracket = forcats::fct_relevel(ageBracket,
"100", "90", "80", "70", "60",
"50", "40", "30", "20", "10",
"0-9")) %>%
dplyr::filter(ageBracket != "Unknown") %>%
dplyr::group_by(pref, ageBracket, cluster) %>%
dplyr::summarise(n = n()) %>%
dplyr::mutate(pref = forcats::fct_rev(pref)) %>%
ggplot2::ggplot(ggplot2::aes(x = pref)) +
ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity",
position = "fill") +
ggplot2::scale_fill_brewer(palette = "Set3") +
ggplot2::scale_y_continuous(labels = scales::percent) +
ggplot2::coord_flip() +
ggplot2::geom_hline(yintercept = c(0.25, 0.5, 0.75), size = 0.35,
colour = "dark gray") +
ggplot2::labs(title = paste0("年齢不明者をのぞく年代構成比 @", datetime),
subtitle = subtitle, caption = caption,
x = "", y = "")
g_age_by_pref_wo
g_age_by_pref_wo +
ggplot2::facet_grid(~ cluster) +
ggplot2::labs(title = paste0("年齢不明者をのぞく非クラスタ/クラスタでの年代構成比 @", datetime))
Visualize
前日差
x_by_region %>%
ggplot2::ggplot(ggplot2::aes(x = date)) +
ggplot2::geom_line(ggplot2::aes(y = diff, colour = region)) +
ggplot2::facet_wrap(~ region, scales = "free_y") +
ggplot2::theme(legend.position = 'none') +
ggplot2::labs(title = paste0("陽性者数前日差, Free Y scale @", datetime),
caption = caption, x = "", y = "")
都道府県別
単日＋累計
sec_scale <- 100
ncol <- 5
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")
x_by_pref %>%
ggplot2::ggplot(ggplot2::aes(x = date)) +
ggplot2::geom_bar(ggplot2::aes(y = n, fill = pref), stat = "identity",
alpha = 0.25, width = 1.0) +
ggplot2::geom_line(ggplot2::aes(y = ma7, colour = pref),
linetype = "solid", size = 0.25) +
ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = pref)) +
ggplot2::facet_wrap(~ pref, ncol = ncol) +
ggplot2::theme(legend.position = 'none') +
ggplot2::labs(title = paste0("Fixed scale @", datetime), caption = caption,
x = "", y = "") +
ggplot2::scale_y_continuous(
name = "陽性者数・移動平均（細線）",
sec.axis = ggplot2::sec_axis(~ . * sec_scale,
name = "累積陽性者数（折線）")
)
x_by_pref %>%
ggplot2::ggplot(ggplot2::aes(x = date)) +
ggplot2::geom_bar(ggplot2::aes(y = n, fill = pref), stat = "identity",
alpha = 0.35, width = 1.0) +
ggplot2::geom_line(ggplot2::aes(y = ma7, colour = pref),
linetype = "solid", size = 0.25) +
ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = pref)) +
ggplot2::facet_wrap(~ pref, ncol = ncol, scales = "free_y") +
ggplot2::theme(legend.position = 'none') +
ggplot2::labs(title = paste0("Free Y scale @", datetime), caption = caption,
x = "", y = "") +
ggplot2::scale_y_continuous(
name = "陽性者数・移動平均（細線）",
sec.axis = ggplot2::sec_axis(~ . * sec_scale,
name = "累積陽性者数（折線）")
)
前日差
x_by_pref %>%
ggplot2::ggplot(ggplot2::aes(x = date)) +
ggplot2::geom_line(ggplot2::aes(y = diff, colour = pref)) +
ggplot2::facet_wrap(~ pref, ncol = ncol, scales = "free_y") +
ggplot2::theme(legend.position = 'none') +
ggplot2::labs(title = paste0("陽性者数前日差, Free Y scale @", datetime),
x = "", y = "")
死亡者の日次推移
全国
sec_scale <- 100
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")
d_by_all %>%
ggplot2::ggplot(ggplot2::aes(x = date)) +
ggplot2::geom_bar(ggplot2::aes(y = n), stat = "identity", width = 1.0,
alpha = 0.5) +
ggplot2::geom_line(ggplot2::aes(y = ma7), linetype = "dashed",
colour = "dark green", size = 0.5) +
ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale),
colour = "dark green", size = 1.0) +
ggplot2::labs(title = paste0("全国の死亡者数推移（単日） @", datetime),
subtitle = subtitle, caption = caption,
x = "", y = "") +
ggplot2::scale_y_continuous(
name = "死亡者数・移動平均（破線）",
sec.axis = ggplot2::sec_axis(~ . * sec_scale,
name = "累積死亡者数（折線）")
)
d_by_all %>%
ggplot2::ggplot(ggplot2::aes(x = date)) +
ggplot2::geom_line(ggplot2::aes(y = diff), colour = "dark green", alpha = 0.5) +
ggplot2::labs(title = paste0("全国の死亡者数前日差 @", datetime),
subtitle = subtitle, caption = caption,
x = "", y = "前日差")
地方別
sec_scale <- 50
ncol <- 4
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")
d_by_region %>%
ggplot2::ggplot(ggplot2::aes(x = date)) +
ggplot2::geom_bar(ggplot2::aes(y = n, fill = region), stat = "identity",
alpha = 0.25, width = 1.0) +
ggplot2::geom_line(ggplot2::aes(y = ma7, colour = region),
linetype = "solid", size = 0.2) +
ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = region)) +
ggplot2::facet_wrap(~ region, ncol = ncol) +
ggplot2::theme(legend.position = 'none') +
ggplot2::labs(title = paste0("Fixed scale @", datetime),
subtitle = subtitle, caption = caption,
x = "", y = "") +
ggplot2::scale_y_continuous(
name = "死亡者数・移動平均（細線）",
sec.axis = ggplot2::sec_axis(~ . * sec_scale,
name = "累積死亡者数（折線）")
)
d_by_region %>%
ggplot2::ggplot(ggplot2::aes(x = date)) +
ggplot2::geom_bar(ggplot2::aes(y = n, fill = region), stat = "identity",
alpha = 0.25, width = 1.0) +
ggplot2::geom_line(ggplot2::aes(y = ma7, colour = region),
linetype = "solid", size = 0.2) +
ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = region)) +
ggplot2::facet_wrap(~ region, ncol = ncol, scales = "free_y") +
ggplot2::theme(legend.position = 'none') +
ggplot2::labs(title = paste0("Free Y scale @", datetime),
subtitle = subtitle, caption = caption,
x = "", y = "") +
ggplot2::scale_y_continuous(
name = "死亡者数・移動平均（細線）",
sec.axis = ggplot2::sec_axis(~ . * sec_scale,
name = "累積死亡者数（折線）")
)
都道府県別日次推移
sec_scale <- 10
ncol <- 5
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")
d_by_prefs %>%
ggplot2::ggplot(ggplot2::aes(x = date)) +
ggplot2::geom_bar(ggplot2::aes(y = n, fill = pref), stat = "identity",
alpha = 0.25, width = 1.0) +
ggplot2::geom_line(ggplot2::aes(y = ma7, colour = pref),
linetype = "solid", size = 0.25) +
ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = pref)) +
ggplot2::facet_wrap(~ pref, ncol = ncol) +
ggplot2::theme(legend.position = 'none') +
ggplot2::labs(title = paste0("Fixed scale @", datetime), caption = caption,
x = "", y = "") +
ggplot2::scale_y_continuous(
name = "死亡者数（単日）",
sec.axis = ggplot2::sec_axis(~ . * sec_scale,
name = "累積死亡者数（折線）")
)
d_by_prefs %>%
ggplot2::ggplot(ggplot2::aes(x = date)) +
ggplot2::geom_bar(ggplot2::aes(y = n, fill = pref), stat = "identity",
alpha = 0.35, width = 1.0) +
ggplot2::geom_line(ggplot2::aes(y = ma7, colour = pref),
linetype = "solid", size = 0.25) +
ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = pref)) +
ggplot2::facet_wrap(~ pref, ncol = ncol, scales = "free_y") +
ggplot2::theme(legend.position = 'none') +
ggplot2::labs(title = paste0("Free Y scale @", datetime), caption = caption,
x = "", y = "") +
ggplot2::scale_y_continuous(
name = "死亡者数（単日）",
sec.axis = ggplot2::sec_axis(~ . * sec_scale,
name = "累積死亡者数（折線）")
)
比較
陽性者数と死亡者の比較。
全国
sec_scale <- (1 / 50)
x_by_all %>%
dplyr::left_join(d_by_all, by = c("date")) %>%
ggplot2::ggplot(ggplot2::aes(x = date)) +
ggplot2::geom_bar(ggplot2::aes(y = n.x), stat = "identity",
fill = "dark green", alpha = 0.25, width = 1.0) +
ggplot2::geom_bar(ggplot2::aes(y = n.y / sec_scale), stat = "identity",
fill = "dark red", alpha = 0.25, width = 1.0) +
# ggplot2::geom_line(ggplot2::aes(y = n.x), colour = "dark green") +
# ggplot2::geom_line(ggplot2::aes(y = n.y / sec_scale), colour = "dark red") +
ggplot2::labs(title = paste0("@", datetime), caption = caption,
x = "", y = "") +
ggplot2::scale_y_continuous(
name = "陽性者数（濃緑）",
sec.axis = ggplot2::sec_axis(~ . * sec_scale,
name = "死亡者数（濃赤）")
)
地方別
sec_scale <- (1 / 10)
ncol <- 4
x_by_region %>%
dplyr::left_join(d_by_region, by = c("date" = "date", "region" = "region")) %>%
ggplot2::ggplot(ggplot2::aes(x = date)) +
ggplot2::geom_bar(ggplot2::aes(y = n.x), stat = "identity",
fill = "dark green", alpha = 0.25, width = 1.0) +
ggplot2::geom_bar(ggplot2::aes(y = n.y / sec_scale), stat = "identity",
fill = "dark red", alpha = 0.25, width = 1.0) +
# ggplot2::geom_line(ggplot2::aes(y = n.x), colour = "dark green") +
# ggplot2::geom_line(ggplot2::aes(y = n.y / sec_scale), colour = "dark red") +
ggplot2::facet_wrap(~ region, ncol = ncol, scales = "free_y") +
ggplot2::labs(title = paste0("Free Y scale @", datetime), caption = caption,
x = "", y = "") +
ggplot2::scale_y_continuous(
name = "陽性者数（濃緑）",
sec.axis = ggplot2::sec_axis(~ . * sec_scale,
name = "死亡者数（濃赤）")
)
相関
地方区分別
r_by_region %>%
ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) +
ggplot2::geom_abline(slope = 1, intercept = 0, colour = "gray") +
ggplot2::geom_point(ggplot2::aes(colour = region)) +
ggrepel::geom_text_repel(ggplot2::aes(label = region, colour = region)) +
ggplot2::theme(legend.position = 'none') +
ggplot2::labs(title = paste0("推計人口と陽性者数 @", datetime),
subtitle = subtitle, caption = caption,
x = "推計人口[千人]", y = "累計陽性者数")
rpd_by_region %>%
ggplot2::ggplot(ggplot2::aes(x = positive, y = deceased)) +
ggplot2::geom_point(ggplot2::aes(colour = region)) +
ggrepel::geom_text_repel(ggplot2::aes(label = region, colour = region)) +
ggplot2::theme(legend.position = 'none') +
ggplot2::labs(title = paste0("陽性者数と死亡者数 @", datetime),
subtitle = subtitle, caption = caption,
x = "陽性者数", y = "死亡者数")
都道府県別
r_by_pref %>%
ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) +
ggplot2::geom_abline(slope = 1, intercept = 0, colour = "gray") +
ggplot2::geom_point(ggplot2::aes(colour = pref)) +
ggrepel::geom_text_repel(ggplot2::aes(label = pref, colour = pref)) +
ggplot2::theme(legend.position = 'none') +
ggplot2::labs(title = paste0("@", datetime), caption = caption,
x = "推計人口[千人]", y = "累計陽性者数")
r_by_pref %>%
dplyr::filter(n < 5000) %>%
ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) +
ggplot2::geom_abline(slope = 1, intercept = 0, colour = "gray") +
ggplot2::geom_point(ggplot2::aes(colour = pref)) +
ggrepel::geom_text_repel(ggplot2::aes(label = pref, colour = pref)) +
ggplot2::theme(legend.position = 'none') +
ggplot2::labs(title = paste0("累計陽性者数五千人未満 @", datetime),
caption = caption,
x = "推計人口[千人]", y = "累計陽性者数")
rpd_by_prefs %>%
ggplot2::ggplot(ggplot2::aes(x = positive, y = deceased)) +
ggplot2::geom_abline(slope = rpd_by_all$d_rate, intercept = 0, colour = "gray") +
ggplot2::geom_point(ggplot2::aes(colour = pref)) +
ggrepel::geom_text_repel(ggplot2::aes(label = pref, colour = pref)) +
ggplot2::theme(legend.position = 'none') +
ggplot2::labs(title = paste0("陽性者数と死亡者数 @", datetime),
subtitle = subtitle, caption = caption,
x = "陽性者数", y = "死亡者数")
rpd_by_prefs %>%
dplyr::filter(positive < 1000) %>%
ggplot2::ggplot(ggplot2::aes(x = positive, y = deceased)) +
ggplot2::geom_abline(slope = rpd_by_all$d_rate, intercept = 0, colour = "gray") +
ggplot2::geom_point(ggplot2::aes(colour = pref)) +
ggrepel::geom_text_repel(ggplot2::aes(label = pref, colour = pref)) +
ggplot2::theme(legend.position = 'none') +
ggplot2::labs(title = paste0("陽性者数と死亡者数 @", datetime),
subtitle = subtitle, caption = caption,
x = "陽性者数", y = "死亡者数")
Model
時系列（TS）分析
日本の時系列データは週単位の変動が認められるので、frequencyを7に設定して陽性者数のデータをtsオブジェクトに変換する。
ts_week <- x_by_all %>%
dplyr::select(n) %>%
ts(frequency = 7)時系列データに変換したものをプロットすると可視化の項でプロットした棒グラフと同じような形のグラフになることが分かります。
ts_week %>%
plot(main = paste0("全国 @", datetime))
上記からトレンド（長期的傾向）を除いたグラフ。デフォルト指定なのでlag = 1。つまり、前日差。
ts_week %>%
base::diff() %>%
plot(main = paste0("全国 @", datetime))
トレンド、季節変動（周期変動）、非周期変動に分解した場合。frequency = 1では分解できない点に注意。
ts_week %>%
stats::decompose() %>%
plot()
トレンドを抜き出してみる。移動平均に酷似している。
ymax <- 3000
ts_week %>%
stats::decompose() %>%
.$x %>%
plot(ylim = c(0, ymax), main = paste0("全国 @", datetime))
par(new = TRUE)
ts_week %>%
stats::decompose() %>%
.$trend %>%
plot(ylim = c(0, ymax), col = "dark green", lwd = 3)
地方別時系列分析
x_by_region %>%
dplyr::select(region, n) %>%
split(.$region) %>%
purrr::map(., ~ ts(.$n, frequency = 7)) %>%
purrr::map2(., paste0(names(.), " @", datetime),
function(.x, name) {
plot(.x, main = name)
} )
## $北海道地方
## NULL
##
## $東北地方
## NULL
##
## $関東地方
## NULL
##
## $中部地方
## NULL
##
## $近畿地方
## NULL
##
## $中国地方
## NULL
##
## $四国地方
## NULL
##
## $九州地方
## NULLoldpar <- par()
par(mfrow=c(4, 2))
x_by_region %>%
dplyr::select(region, n) %>%
split(.$region) %>%
purrr::map(., ~ ts(.$n, frequency = 7)) %>%
purrr::map2(., paste0(names(.), " @", datetime),
function(.x, .y) {
plot(.x, main = .y, ylim = c(0, max(.x)), col = "dark gray")
par(new = TRUE)
stats::decompose(.x) %>%
.$trend %>%
plot(ylim = c(0, max(.x)), col = "dark green", lwd = 2)
} )
## $北海道地方
## NULL
##
## $東北地方
## NULL
##
## $関東地方
## NULL
##
## $中部地方
## NULL
##
## $近畿地方
## NULL
##
## $中国地方
## NULL
##
## $四国地方
## NULL
##
## $九州地方
## NULLpar(oldpar)x_by_pref %>%
dplyr::select(pref, n) %>%
split(.$pref) %>%
purrr::map(., ~ ts(.$n, frequency = 7)) %>%
purrr::map2(., paste0(names(.), " @", datetime),
function(.x, .y) {
plot(.x, main = .y, ylim = c(0, max(.x)), col = "dark gray")
# plot(.x, main = region)
par(new = TRUE)
stats::decompose(.x) %>%
.$trend %>%
plot(ylim = c(0, max(.x)), col = "dark green", lwd = 2)
} )
## $北海道
## NULL
##
## $青森県
## NULL
##
## $岩手県
## NULL
##
## $宮城県
## NULL
##
## $秋田県
## NULL
##
## $山形県
## NULL
##
## $福島県
## NULL
##
## $茨城県
## NULL
##
## $栃木県
## NULL
##
## $群馬県
## NULL
##
## $埼玉県
## NULL
##
## $千葉県
## NULL
##
## $東京都
## NULL
##
## $神奈川県
## NULL
##
## $新潟県
## NULL
##
## $富山県
## NULL
##
## $石川県
## NULL
##
## $福井県
## NULL
##
## $山梨県
## NULL
##
## $長野県
## NULL
##
## $岐阜県
## NULL
##
## $静岡県
## NULL
##
## $愛知県
## NULL
##
## $三重県
## NULL
##
## $滋賀県
## NULL
##
## $京都府
## NULL
##
## $大阪府
## NULL
##
## $兵庫県
## NULL
##
## $奈良県
## NULL
##
## $和歌山県
## NULL
##
## $鳥取県
## NULL
##
## $島根県
## NULL
##
## $岡山県
## NULL
##
## $広島県
## NULL
##
## $山口県
## NULL
##
## $徳島県
## NULL
##
## $香川県
## NULL
##
## $愛媛県
## NULL
##
## $高知県
## NULL
##
## $福岡県
## NULL
##
## $佐賀県
## NULL
##
## $長崎県
## NULL
##
## $熊本県
## NULL
##
## $大分県
## NULL
##
## $宮崎県
## NULL
##
## $鹿児島県
## NULL
##
## $沖縄県
## NULL
Infer
時系列予測（ARIMA）
ARIMA（Auto Regressive Integrated Moving Average, 自己回帰和分移動平均）モデルによる陽性者に対する予測。予測に必要なパラメータはステップワイズにより自動的に最適なものが選択される。ただし、モデル自体を評価していないので、こういうことが出来る程度の話。
全国
x_by_all %>%
dplyr::select(n) %>%
ts(.$n, frequency = 7) %>%
forecast::auto.arima() %>%
forecast::forecast() %>%
plot(main = paste0("全国 @", datetime))
地方別
x_by_region %>%
dplyr::select(region, n) %>%
split(.$region) %>%
purrr::map(., ~ ts(.$n, frequency = 7)) %>%
purrr::map(., forecast::auto.arima) %>%
purrr::map(., forecast::forecast) %>%
purrr::map2(., paste0(names(.), " @", datetime),
function(.x, name) {
plot(.x, main = name)
} )
## $北海道地方
## $北海道地方$mean
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## [1] 84.09957 93.88590 102.43703 97.99809 99.53812 84.67913 85.51396
## [8] 82.52511 92.41569 88.14116 84.60335 87.03831 82.73852 82.87171
##
## $北海道地方$lower
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 62.95736 51.765356
## 55.00000 68.86618 55.621538
## 55.14286 73.05471 57.500648
## 55.28571 67.49164 51.342495
## 55.42857 68.01437 51.326711
## 55.57143 51.71976 34.272125
## 55.71429 50.82754 32.465665
## 55.85714 44.69161 24.663757
## 56.00000 52.40718 31.227959
## 56.14286 46.13102 23.892202
## 56.28571 41.10121 18.072570
## 56.42857 42.05430 18.241204
## 56.57143 36.17043 11.518776
## 56.71429 34.69269 9.188258
##
## $北海道地方$upper
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 105.2418 116.4338
## 55.00000 118.9056 132.1503
## 55.14286 131.8194 147.3734
## 55.28571 128.5045 144.6537
## 55.42857 131.0619 147.7495
## 55.57143 117.6385 135.0861
## 55.71429 120.2004 138.5623
## 55.85714 120.3586 140.3865
## 56.00000 132.4242 153.6034
## 56.14286 130.1513 152.3901
## 56.28571 128.1055 151.1341
## 56.42857 132.0223 155.8354
## 56.57143 129.3066 153.9583
## 56.71429 131.0507 156.5552
##
##
## $東北地方
## $東北地方$mean
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## [1] 59.16378 76.95214 80.30641 76.42279 81.45715 74.42021 69.20570 73.84424
## [9] 74.56741 71.11011 70.91620 73.90405 68.56006 66.81009
##
## $東北地方$lower
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 44.68727 37.02386
## 55.00000 59.75640 50.65353
## 55.14286 62.67887 53.34742
## 55.28571 58.37377 48.81920
## 55.42857 62.99628 53.22369
## 55.57143 55.55648 45.57062
## 55.71429 49.94753 39.75287
## 55.85714 54.04341 43.56148
## 56.00000 54.29584 43.56471
## 56.14286 50.43355 39.48805
## 56.28571 49.84244 38.68667
## 56.42857 52.44045 41.07831
## 56.57143 46.71357 35.14873
## 56.71429 44.58730 32.82326
##
## $東北地方$upper
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 73.64029 81.30370
## 55.00000 94.14788 103.25076
## 55.14286 97.93395 107.26541
## 55.28571 94.47180 104.02637
## 55.42857 99.91801 109.69061
## 55.57143 93.28394 103.26979
## 55.71429 88.46387 98.65852
## 55.85714 93.64508 104.12700
## 56.00000 94.83899 105.57011
## 56.14286 91.78666 102.73217
## 56.28571 91.98995 103.14572
## 56.42857 95.36765 106.72980
## 56.57143 90.40655 101.97139
## 56.71429 89.03287 100.79691
##
##
## $関東地方
## $関東地方$mean
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## [1] 2017.631 2138.003 2673.913 2614.120 2554.716 2108.996 1752.293 2072.997
## [9] 2264.162 2736.419 2645.103 2570.086 2116.633 1756.099
##
## $関東地方$lower
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 1846.186 1755.4290
## 55.00000 1887.451 1754.8167
## 55.14286 2346.610 2173.3466
## 55.28571 2244.452 2048.7617
## 55.42857 2155.773 1944.5851
## 55.57143 1687.295 1464.0605
## 55.71429 1311.706 1078.4727
## 55.85714 1603.596 1355.1105
## 56.00000 1768.563 1506.2082
## 56.14286 2215.403 1939.5932
## 56.28571 2103.767 1817.2010
## 56.42857 2011.573 1715.9137
## 56.57143 1543.137 1239.5462
## 56.71429 1169.301 858.6693
##
## $関東地方$upper
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 2189.076 2279.833
## 55.00000 2388.555 2521.190
## 55.14286 3001.215 3174.479
## 55.28571 2983.788 3179.479
## 55.42857 2953.660 3164.847
## 55.57143 2530.697 2753.932
## 55.71429 2192.881 2426.114
## 55.85714 2542.398 2790.884
## 56.00000 2759.762 3022.116
## 56.14286 3257.436 3533.245
## 56.28571 3186.439 3473.005
## 56.42857 3128.599 3424.258
## 56.57143 2690.129 2993.719
## 56.71429 2342.897 2653.529
##
##
## $中部地方
## $中部地方$mean
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## [1] 281.4343 414.9156 445.1890 426.9105 403.9212 317.3914 199.6552 296.5903
## [9] 428.7074 457.7392 438.3311 414.3138 326.8485 208.2610
##
## $中部地方$lower
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 240.0556 218.15107
## 55.00000 358.9691 329.35278
## 55.14286 379.5835 344.85407
## 55.28571 354.2725 315.82021
## 55.42857 325.9383 284.65656
## 55.57143 235.2453 191.75971
## 55.71429 114.2150 68.98570
## 55.85714 204.4710 155.70603
## 56.00000 331.4038 279.89433
## 56.14286 356.3430 302.66711
## 56.28571 333.6669 278.26110
## 56.42857 307.0189 250.22040
## 56.57143 217.4230 159.49664
## 56.71429 97.1021 38.25814
##
## $中部地方$upper
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 322.8129 344.7175
## 55.00000 470.8622 500.4785
## 55.14286 510.7944 545.5239
## 55.28571 499.5486 538.0008
## 55.42857 481.9042 523.1859
## 55.57143 399.5376 443.0232
## 55.71429 285.0954 330.3247
## 55.85714 388.7095 437.4745
## 56.00000 526.0110 577.5204
## 56.14286 559.1355 612.8114
## 56.28571 542.9952 598.4011
## 56.42857 521.6087 578.4072
## 56.57143 436.2740 494.2004
## 56.71429 319.4199 378.2639
##
##
## $近畿地方
## $近畿地方$mean
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## [1] 686.4348 865.6610 903.2188 919.4268 943.4875 817.6410 628.7201
## [8] 783.5603 955.2836 986.2918 996.7823 1015.8518 885.6482 692.9238
##
## $近畿地方$lower
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 619.7947 584.5175
## 55.00000 776.4215 729.1810
## 55.14286 799.4316 744.4900
## 55.28571 805.3121 744.9034
## 55.42857 821.6839 757.2049
## 55.57143 689.9402 622.3394
## 55.71429 496.3981 426.3511
## 55.85714 641.2236 565.8751
## 56.00000 805.2828 725.8771
## 56.14286 830.2848 747.6997
## 56.28571 835.9804 750.8570
## 56.42857 851.1618 763.9802
## 56.57143 717.7617 628.8879
## 56.71429 522.3755 432.0928
##
## $近畿地方$upper
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 753.0750 788.3522
## 55.00000 954.9004 1002.1410
## 55.14286 1007.0060 1061.9476
## 55.28571 1033.5415 1093.9502
## 55.42857 1065.2911 1129.7700
## 55.57143 945.3418 1012.9425
## 55.71429 761.0421 831.0891
## 55.85714 925.8970 1001.2455
## 56.00000 1105.2844 1184.6901
## 56.14286 1142.2988 1224.8839
## 56.28571 1157.5842 1242.7076
## 56.42857 1180.5417 1267.7233
## 56.57143 1053.5347 1142.4085
## 56.71429 863.4721 953.7549
##
##
## $中国地方
## $中国地方$mean
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## [1] 66.22159 73.35188 67.03076 72.63453 67.66670 72.07076 68.16649 71.62769
## [9] 68.55928 71.27948 68.86798 71.00581 69.11059 70.79073
##
## $中国地方$lower
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 48.41028 38.98154
## 55.00000 53.68682 43.27677
## 55.14286 46.27300 35.28450
## 55.28571 50.33659 38.53277
## 55.42857 44.35027 32.00730
## 55.57143 47.42246 34.37444
## 55.71429 42.55722 29.00049
## 55.85714 44.83517 30.65206
## 56.00000 40.85360 26.18709
## 56.14286 42.50183 27.26786
## 56.28571 39.21854 23.52308
## 56.42857 40.37079 24.15359
## 56.57143 37.64044 20.98116
## 56.71429 38.40396 21.25944
##
## $中国地方$upper
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 84.03290 93.46164
## 55.00000 93.01693 103.42698
## 55.14286 87.78853 98.77703
## 55.28571 94.93247 106.73629
## 55.42857 90.98313 103.32611
## 55.57143 96.71907 109.76709
## 55.71429 93.77576 107.33249
## 55.85714 98.42021 112.60332
## 56.00000 96.26497 110.93147
## 56.14286 100.05712 115.29109
## 56.28571 98.51742 114.21288
## 56.42857 101.64083 117.85803
## 56.57143 100.58074 117.24002
## 56.71429 103.17750 120.32202
##
##
## $四国地方
## $四国地方$mean
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## [1] 24.81511 34.07880 34.01406 32.77706 35.07799 32.12425 30.11429 32.70802
## [9] 33.80731 34.51262 34.96515 35.25549 35.44177 35.56129
##
## $四国地方$lower
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 16.86977 12.66376
## 55.00000 24.90855 20.05412
## 55.14286 24.19608 18.99876
## 55.28571 22.56228 17.15491
## 55.42857 24.59002 19.03803
## 55.57143 21.42906 15.76736
## 55.71429 19.24926 13.49766
## 55.85714 21.30891 15.27458
## 56.00000 22.09880 15.90069
## 56.14286 22.56003 16.23271
## 56.28571 22.80640 16.36996
## 56.42857 22.91364 16.38027
## 56.57143 22.93140 16.30880
## 56.71429 22.89195 16.18521
##
## $四国地方$upper
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 32.76045 36.96645
## 55.00000 43.24904 48.10348
## 55.14286 43.83203 49.02935
## 55.28571 42.99183 48.39920
## 55.42857 45.56596 51.11796
## 55.57143 42.81945 48.48114
## 55.71429 40.97932 46.73092
## 55.85714 44.10712 50.14145
## 56.00000 45.51582 51.71393
## 56.14286 46.46521 52.79253
## 56.28571 47.12389 53.56034
## 56.42857 47.59733 54.13071
## 56.57143 47.95214 54.57473
## 56.71429 48.23062 54.93737
##
##
## $九州地方
## $九州地方$mean
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## [1] 285.5852 373.7715 429.5432 435.1524 409.6038 336.7856 262.8872 280.5064
## [9] 350.6102 395.0314 401.6583 367.3752 308.9384 254.4277
##
## $九州地方$lower
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 247.3764 227.14990
## 55.00000 318.5643 289.33937
## 55.14286 362.3618 326.79820
## 55.28571 361.4426 322.42289
## 55.42857 331.2974 289.84444
## 55.57143 255.5958 212.61646
## 55.71429 179.4588 135.29437
## 55.85714 191.8124 144.86061
## 56.00000 255.2102 204.70856
## 56.14286 292.2109 237.78104
## 56.28571 292.6022 234.87139
## 56.42857 253.7718 193.63381
## 56.57143 192.2899 130.53985
## 56.71429 135.2512 72.16301
##
## $九州地方$upper
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 323.7940 344.0205
## 55.00000 428.9786 458.2036
## 55.14286 496.7245 532.2882
## 55.28571 508.8623 547.8820
## 55.42857 487.9102 529.3631
## 55.57143 417.9754 460.9547
## 55.71429 346.3157 390.4801
## 55.85714 369.2004 416.1522
## 56.00000 446.0102 496.5118
## 56.14286 497.8519 552.2818
## 56.28571 510.7144 568.4452
## 56.42857 480.9786 541.1166
## 56.57143 425.5869 487.3369
## 56.71429 373.6042 436.6924
都道府県別
x_by_pref %>%
dplyr::select(pref, n) %>%
split(.$pref) %>%
purrr::map(., ~ ts(.$n, frequency = 7)) %>%
purrr::map(., forecast::auto.arima) %>%
purrr::map(., forecast::forecast) %>%
purrr::map2(., paste0(names(.), " @", datetime),
function(.x, name) {
plot(.x, main = name)
} )
## $北海道
## $北海道$mean
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## [1] 84.09957 93.88590 102.43703 97.99809 99.53812 84.67913 85.51396
## [8] 82.52511 92.41569 88.14116 84.60335 87.03831 82.73852 82.87171
##
## $北海道$lower
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 62.95736 51.765356
## 55.00000 68.86618 55.621538
## 55.14286 73.05471 57.500648
## 55.28571 67.49164 51.342495
## 55.42857 68.01437 51.326711
## 55.57143 51.71976 34.272125
## 55.71429 50.82754 32.465665
## 55.85714 44.69161 24.663757
## 56.00000 52.40718 31.227959
## 56.14286 46.13102 23.892202
## 56.28571 41.10121 18.072570
## 56.42857 42.05430 18.241204
## 56.57143 36.17043 11.518776
## 56.71429 34.69269 9.188258
##
## $北海道$upper
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 105.2418 116.4338
## 55.00000 118.9056 132.1503
## 55.14286 131.8194 147.3734
## 55.28571 128.5045 144.6537
## 55.42857 131.0619 147.7495
## 55.57143 117.6385 135.0861
## 55.71429 120.2004 138.5623
## 55.85714 120.3586 140.3865
## 56.00000 132.4242 153.6034
## 56.14286 130.1513 152.3901
## 56.28571 128.1055 151.1341
## 56.42857 132.0223 155.8354
## 56.57143 129.3066 153.9583
## 56.71429 131.0507 156.5552
##
##
## $青森県
## $青森県$mean
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## [1] 4.653267 4.631400 4.631400 4.631400 4.631400 4.631400 4.631400 4.631400
## [9] 4.631400 4.631400 4.631400 4.631400 4.631400 4.631400
##
## $青森県$lower
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 1.12847634 -0.737435
## 55.00000 0.79674667 -1.233196
## 55.14286 0.63100160 -1.486681
## 55.28571 0.47185572 -1.730074
## 55.42857 0.31857844 -1.964491
## 55.57143 0.17056476 -2.190859
## 55.71429 0.02730701 -2.409953
## 55.85714 -0.11162577 -2.622432
## 56.00000 -0.24660314 -2.828862
## 56.14286 -0.37794485 -3.029732
## 56.28571 -0.50592976 -3.225468
## 56.42857 -0.63080280 -3.416445
## 56.57143 -0.75278049 -3.602993
## 56.71429 -0.87205534 -3.785409
##
## $青森県$upper
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 8.178057 10.04397
## 55.00000 8.466053 10.49600
## 55.14286 8.631798 10.74948
## 55.28571 8.790944 10.99287
## 55.42857 8.944221 11.22729
## 55.57143 9.092235 11.45366
## 55.71429 9.235493 11.67275
## 55.85714 9.374426 11.88523
## 56.00000 9.509403 12.09166
## 56.14286 9.640745 12.29253
## 56.28571 9.768730 12.48827
## 56.42857 9.893603 12.67924
## 56.57143 10.015580 12.86579
## 56.71429 10.134855 13.04821
##
##
## $岩手県
## $岩手県$mean
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## [1] 2.609322 3.628067 3.399897 3.422525 3.633826 3.442847 3.438162 3.653589
## [9] 3.746726 3.763782 3.781609 3.778252 3.784646 3.780847
##
## $岩手県$lower
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 -0.8485390 -2.679020
## 55.00000 -0.2245344 -2.263978
## 55.14286 -0.5034127 -2.569700
## 55.28571 -0.5309732 -2.623829
## 55.42857 -0.3337629 -2.434078
## 55.57143 -0.5520910 -2.666883
## 55.71429 -0.5700941 -2.691936
## 55.85714 -0.4247818 -2.583741
## 56.00000 -0.3625984 -2.537943
## 56.14286 -0.3719638 -2.561295
## 56.28571 -0.3748982 -2.575220
## 56.42857 -0.4002540 -2.612221
## 56.57143 -0.4137347 -2.636223
## 56.71429 -0.4385646 -2.672186
##
## $岩手県$upper
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 6.067182 7.897663
## 55.00000 7.480669 9.520113
## 55.14286 7.303208 9.369495
## 55.28571 7.376023 9.468878
## 55.42857 7.601416 9.701731
## 55.57143 7.437784 9.552576
## 55.71429 7.446417 9.568260
## 55.85714 7.731959 9.890918
## 56.00000 7.856050 10.031395
## 56.14286 7.899528 10.088860
## 56.28571 7.938115 10.138437
## 56.42857 7.956758 10.168725
## 56.57143 7.983026 10.205514
## 56.71429 8.000259 10.233880
##
##
## $宮城県
## $宮城県$mean
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## [1] 33.91071 32.80062 36.58555 39.98578 39.33403 36.86938 34.51048 38.42970
## [9] 39.45554 40.46949 41.03046 41.43521 41.71644 41.93134
##
## $宮城県$lower
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 24.38882 19.34824
## 55.00000 22.54299 17.11294
## 55.14286 25.79558 20.08372
## 55.28571 28.95288 23.11242
## 55.42857 28.14312 22.21901
## 55.57143 25.56947 19.58765
## 55.71429 23.12488 17.09771
## 55.85714 26.56629 20.28617
## 56.00000 27.41725 21.04457
## 56.14286 28.28123 21.82916
## 56.28571 28.72980 22.21822
## 56.42857 29.03685 22.47355
## 56.57143 29.23016 22.62033
## 56.71429 29.36260 22.70911
##
## $宮城県$upper
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 43.43260 48.47319
## 55.00000 43.05824 48.48829
## 55.14286 47.37551 53.08738
## 55.28571 51.01868 56.85915
## 55.42857 50.52494 56.44905
## 55.57143 48.16929 54.15110
## 55.71429 45.89608 51.92326
## 55.85714 50.29312 56.57323
## 56.00000 51.49382 57.86651
## 56.14286 52.65775 59.10983
## 56.28571 53.33112 59.84270
## 56.42857 53.83357 60.39687
## 56.57143 54.20272 60.81255
## 56.71429 54.50007 61.15356
##
##
## $秋田県
## $秋田県$mean
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## [1] 4.529911 4.471416 4.691194 5.241360 4.909921 4.909921 4.909921 4.909921
## [9] 4.909921 4.909921 4.909921 4.909921 4.909921 4.909921
##
## $秋田県$lower
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 2.721675 1.764452
## 55.00000 2.608314 1.622047
## 55.14286 2.780616 1.769217
## 55.28571 3.290620 2.257960
## 55.42857 2.958714 1.925807
## 55.57143 2.942510 1.901026
## 55.71429 2.926439 1.876447
## 55.85714 2.910497 1.852065
## 56.00000 2.894680 1.827876
## 56.14286 2.878987 1.803876
## 56.28571 2.863415 1.780060
## 56.42857 2.847960 1.756423
## 56.57143 2.832620 1.732963
## 56.71429 2.817392 1.709674
##
## $秋田県$upper
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 6.338148 7.295371
## 55.00000 6.334518 7.320785
## 55.14286 6.601772 7.613171
## 55.28571 7.192100 8.224760
## 55.42857 6.861129 7.894036
## 55.57143 6.877333 7.918817
## 55.71429 6.893404 7.943396
## 55.85714 6.909346 7.967778
## 56.00000 6.925162 7.991967
## 56.14286 6.940855 8.015967
## 56.28571 6.956428 8.039783
## 56.42857 6.971883 8.063420
## 56.57143 6.987223 8.086880
## 56.71429 7.002451 8.110169
##
##
## $山形県
## $山形県$mean
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## [1] 0.9052592 0.9344471 0.8886108 1.0068324 1.0068324 1.0068324 1.0068324
## [8] 1.0068324 1.0068324 1.0068324 1.0068324 1.0068324 1.0068324 1.0068324
##
## $山形県$lower
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 -1.228086 -2.357410
## 55.00000 -1.275518 -2.445403
## 55.14286 -1.442816 -2.676999
## 55.28571 -1.506464 -2.836923
## 55.42857 -1.610463 -2.995976
## 55.57143 -1.710485 -3.148946
## 55.71429 -1.806954 -3.296482
## 55.85714 -1.900223 -3.439125
## 56.00000 -1.990591 -3.577331
## 56.14286 -2.078314 -3.711491
## 56.28571 -2.163610 -3.841940
## 56.42857 -2.246671 -3.968971
## 56.57143 -2.327663 -4.092838
## 56.71429 -2.406734 -4.213767
##
## $山形県$upper
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 3.038604 4.167928
## 55.00000 3.144412 4.314297
## 55.14286 3.220038 4.454221
## 55.28571 3.520129 4.850588
## 55.42857 3.624128 5.009641
## 55.57143 3.724150 5.162611
## 55.71429 3.820619 5.310147
## 55.85714 3.913888 5.452790
## 56.00000 4.004256 5.590996
## 56.14286 4.091978 5.725156
## 56.28571 4.177275 5.855605
## 56.42857 4.260335 5.982636
## 56.57143 4.341328 6.106503
## 56.71429 4.420399 6.227432
##
##
## $福島県
## $福島県$mean
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## [1] 19.18863 22.96926 20.55378 20.67586 22.34498 21.70502 19.41751 19.10914
## [9] 21.16074 19.31707 18.94814 21.25699 19.97221 19.15108
##
## $福島県$lower
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 14.18222 11.531987
## 55.00000 17.51338 14.625216
## 55.14286 14.98896 12.043115
## 55.28571 14.89205 11.830280
## 55.42857 16.28746 13.080810
## 55.57143 15.40781 12.074275
## 55.71429 12.90131 9.451838
## 55.85714 12.29340 8.685370
## 56.00000 14.10321 10.367185
## 56.14286 12.04266 8.191825
## 56.28571 11.45650 7.490658
## 56.42857 13.55092 9.471576
## 56.57143 12.05857 7.869337
## 56.71429 11.03581 6.739840
##
## $福島県$upper
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 24.19504 26.84527
## 55.00000 28.42514 31.31331
## 55.14286 26.11861 29.06445
## 55.28571 26.45967 29.52143
## 55.42857 28.40249 31.60914
## 55.57143 28.00222 31.33576
## 55.71429 25.93371 29.38318
## 55.85714 25.92488 29.53291
## 56.00000 28.21827 31.95430
## 56.14286 26.59147 30.44231
## 56.28571 26.43979 30.40563
## 56.42857 28.96306 33.04240
## 56.57143 27.88585 32.07508
## 56.71429 27.26635 31.56231
##
##
## $茨城県
## $茨城県$mean
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## [1] 55.85879 63.35279 65.08310 67.44045 77.17971 52.54955 50.40529 53.98119
## [9] 57.90529 54.78134 49.22732 65.08983 48.16299 49.73701
##
## $茨城県$lower
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 43.86398 37.51431
## 55.00000 50.27721 43.35542
## 55.14286 51.34760 44.07646
## 55.28571 53.07532 45.47088
## 55.42857 62.21140 54.28766
## 55.57143 37.00145 28.77079
## 55.71429 34.29826 25.77171
## 55.85714 36.13618 26.68960
## 56.00000 39.16762 29.24850
## 56.14286 35.27413 24.94764
## 56.28571 28.97979 18.26140
## 56.42857 44.12811 33.03165
## 56.57143 26.51063 15.04856
## 56.71429 27.41536 15.59900
##
## $茨城県$upper
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 67.85360 74.20326
## 55.00000 76.42837 83.35016
## 55.14286 78.81859 86.08973
## 55.28571 81.80559 89.41003
## 55.42857 92.14801 100.07175
## 55.57143 68.09764 76.32831
## 55.71429 66.51232 75.03887
## 55.85714 71.82621 81.27279
## 56.00000 76.64295 86.56207
## 56.14286 74.28855 84.61504
## 56.28571 69.47485 80.19324
## 56.42857 86.05155 97.14801
## 56.57143 69.81535 81.27742
## 56.71429 72.05866 83.87502
##
##
## $栃木県
## $栃木県$mean
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## [1] 24.784658 13.995109 13.106012 16.230756 12.561232 11.576873 10.091240
## [8] 10.032491 9.957757 9.652632 9.791545 9.733510 9.678462 9.742127
##
## $栃木県$lower
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 15.2330838 10.176786
## 55.00000 3.2261281 -2.474626
## 55.14286 0.6014227 -6.018107
## 55.28571 1.2185448 -6.728440
## 55.42857 -3.6763659 -12.272031
## 55.57143 -6.2506395 -15.687955
## 55.71429 -9.2235864 -19.448238
## 55.85714 -10.9627401 -22.076944
## 56.00000 -12.5509940 -24.466407
## 56.14286 -14.2756869 -26.942574
## 56.28571 -15.5395049 -28.948953
## 56.42857 -16.8893839 -30.982693
## 56.57143 -18.1888645 -32.940936
## 56.71429 -19.3263296 -34.714241
##
## $栃木県$upper
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 34.33623 39.39253
## 55.00000 24.76409 30.46484
## 55.14286 25.61060 32.23013
## 55.28571 31.24297 39.18995
## 55.42857 28.79883 37.39450
## 55.57143 29.40439 38.84170
## 55.71429 29.40607 39.63072
## 55.85714 31.02772 42.14193
## 56.00000 32.46651 44.38192
## 56.14286 33.58095 46.24784
## 56.28571 35.12259 48.53204
## 56.42857 36.35640 50.44971
## 56.57143 37.54579 52.29786
## 56.71429 38.81058 54.19849
##
##
## $群馬県
## $群馬県$mean
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## [1] 37.27017 41.51105 40.02814 42.41329 40.46524 33.13402 26.78507 28.86911
## [9] 30.45001 33.88851 32.48394 31.02344 27.41300 29.89915
##
## $群馬県$lower
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 28.92063 24.500659
## 55.00000 31.37007 26.001766
## 55.14286 29.27926 23.589147
## 55.28571 31.40762 25.581565
## 55.42857 29.23663 23.292555
## 55.57143 21.54924 15.416631
## 55.71429 14.59855 8.147403
## 55.85714 16.08019 9.310150
## 56.00000 16.85923 9.664714
## 56.14286 19.44378 11.797205
## 56.28571 17.26173 9.203573
## 56.42857 15.15472 6.754328
## 56.57143 11.01949 2.341287
## 56.71429 13.06323 4.150836
##
## $群馬県$upper
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 45.61970 50.03967
## 55.00000 51.65203 57.02034
## 55.14286 50.77701 56.46712
## 55.28571 53.41896 59.24501
## 55.42857 51.69386 57.63793
## 55.57143 44.71880 50.85141
## 55.71429 38.97158 45.42273
## 55.85714 41.65802 48.42807
## 56.00000 44.04078 51.23530
## 56.14286 48.33324 55.97981
## 56.28571 47.70615 55.76431
## 56.42857 46.89215 55.29255
## 56.57143 43.80652 52.48472
## 56.71429 46.73507 55.64747
##
##
## $埼玉県
## $埼玉県$mean
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## [1] 330.7798 330.3786 343.6693 321.3917 302.6830 305.1456 304.3857 316.7230
## [9] 324.1916 328.8587 331.9138 334.0414 335.6353 336.9221
##
## $埼玉県$lower
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 297.8809 280.4653
## 55.00000 290.0824 268.7509
## 55.14286 299.6459 276.3413
## 55.28571 275.0295 250.4868
## 55.42857 254.6115 229.1639
## 55.57143 255.6827 229.4986
## 55.71429 253.7053 226.8767
## 55.85714 261.2892 231.9444
## 56.00000 265.6523 234.6634
## 56.14286 267.9795 235.7519
## 56.28571 269.0901 235.8333
## 56.42857 269.4946 235.3256
## 56.57143 269.4984 234.4877
## 56.71429 269.2811 233.4741
##
## $埼玉県$upper
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 363.6787 381.0944
## 55.00000 370.6748 392.0063
## 55.14286 387.6927 410.9973
## 55.28571 367.7540 392.2967
## 55.42857 350.7546 376.2021
## 55.57143 354.6085 380.7926
## 55.71429 355.0661 381.8947
## 55.85714 372.1568 401.5017
## 56.00000 382.7309 413.7198
## 56.14286 389.7379 421.9654
## 56.28571 394.7375 427.9943
## 56.42857 398.5882 432.7572
## 56.57143 401.7721 436.7828
## 56.71429 404.5630 440.3700
##
##
## $千葉県
## $千葉県$mean
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## [1] 369.5295 336.1231 388.1401 385.2818 364.3096 309.7919 276.7183 343.0364
## [9] 304.4748 348.6432 342.4821 319.8019 274.6188 250.9181
##
## $千葉県$lower
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 344.9728 331.9733
## 55.00000 305.4007 289.1373
## 55.14286 353.2910 334.8430
## 55.28571 346.7454 326.3454
## 55.42857 322.4091 300.2283
## 55.57143 264.7781 240.9492
## 55.71429 228.7929 203.4228
## 55.85714 288.3140 259.3457
## 56.00000 244.7444 213.1250
## 56.14286 284.5926 250.6864
## 56.28571 274.3850 238.3365
## 56.42857 247.8855 209.8153
## 56.57143 199.0760 159.0861
## 56.71429 171.9152 130.0935
##
## $千葉県$upper
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 394.0861 407.0856
## 55.00000 366.8454 383.1088
## 55.14286 422.9891 441.4371
## 55.28571 423.8181 444.2181
## 55.42857 406.2100 428.3908
## 55.57143 354.8058 378.6346
## 55.71429 324.6436 350.0138
## 55.85714 397.7589 426.7272
## 56.00000 364.2052 395.8245
## 56.14286 412.6937 446.6000
## 56.28571 410.5792 446.6276
## 56.42857 391.7183 429.7885
## 56.57143 350.1616 390.1516
## 56.71429 329.9211 371.7427
##
##
## $東京都
## $東京都$mean
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## [1] 733.9898 811.7940 996.4336 986.7724 940.9790 826.8859 626.7706 645.2807
## [9] 682.2232 780.2022 727.3609 694.8072 647.0054 505.2117
##
## $東京都$lower
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 618.1474 556.82412
## 55.00000 653.3932 569.54084
## 55.14286 791.0470 682.32195
## 55.28571 763.8375 645.82297
## 55.42857 706.4865 582.35365
## 55.57143 588.1743 461.80799
## 55.71429 383.3911 254.55381
## 55.85714 387.5997 251.19156
## 56.00000 406.9879 261.28716
## 56.14286 483.6879 326.72277
## 56.28571 413.5374 247.40924
## 56.42857 366.5150 192.72759
## 56.57143 307.8888 128.37132
## 56.71429 156.5305 -28.05012
##
## $東京都$upper
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 849.8323 911.1556
## 55.00000 970.1948 1054.0472
## 55.14286 1201.8202 1310.5453
## 55.28571 1209.7072 1327.7218
## 55.42857 1175.4715 1299.6044
## 55.57143 1065.5976 1191.9639
## 55.71429 870.1501 998.9874
## 55.85714 902.9617 1039.3698
## 56.00000 957.4585 1103.1592
## 56.14286 1076.7164 1233.6815
## 56.28571 1041.1844 1207.3125
## 56.42857 1023.0994 1196.8868
## 56.57143 986.1219 1165.6394
## 56.71429 853.8928 1038.4734
##
##
## $神奈川県
## $神奈川県$mean
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## [1] 421.5478 384.8823 430.1320 384.5576 356.8487 366.1978 380.0415 312.4857
## [9] 313.5289 309.4176 273.4569 235.8578 260.7830 206.0528
##
## $神奈川県$lower
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 365.68357 336.11084
## 55.00000 316.57767 280.41940
## 55.14286 351.32709 309.61026
## 55.28571 296.49555 249.87831
## 55.42857 260.41406 209.36466
## 55.57143 262.06166 206.93531
## 55.71429 268.73544 209.81356
## 55.85714 191.94580 128.13585
## 56.00000 185.13954 117.17432
## 56.14286 173.63174 101.75105
## 56.28571 130.65709 55.06346
## 56.42857 86.37289 7.24035
## 56.57143 104.89933 22.37950
## 56.71429 44.02288 -41.75057
##
## $神奈川県$upper
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 477.4120 506.9848
## 55.00000 453.1868 489.3451
## 55.14286 508.9370 550.6538
## 55.28571 472.6197 519.2369
## 55.42857 453.2833 504.3327
## 55.57143 470.3340 525.4603
## 55.71429 491.3476 550.2695
## 55.85714 433.0255 496.8355
## 56.00000 441.9183 509.8835
## 56.14286 445.2035 517.0842
## 56.28571 416.2566 491.8503
## 56.42857 385.3428 464.4753
## 56.57143 416.6667 499.1865
## 56.71429 368.0827 453.8561
##
##
## $新潟県
## $新潟県$mean
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## [1] 9.743094 10.965683 10.965683 10.965683 10.965683 10.965683 10.965683
## [8] 10.965683 10.965683 10.965683 10.965683 10.965683 10.965683 10.965683
##
## $新潟県$lower
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 5.905738 3.874365
## 55.00000 6.888742 4.730540
## 55.14286 6.841818 4.658776
## 55.28571 6.795421 4.587818
## 55.42857 6.749536 4.517642
## 55.57143 6.704144 4.448221
## 55.71429 6.659230 4.379532
## 55.85714 6.614781 4.311552
## 56.00000 6.570780 4.244259
## 56.14286 6.527216 4.177634
## 56.28571 6.484076 4.111656
## 56.42857 6.441346 4.046307
## 56.57143 6.399017 3.981570
## 56.71429 6.357076 3.917427
##
## $新潟県$upper
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 13.58045 15.61182
## 55.00000 15.04262 17.20083
## 55.14286 15.08955 17.27259
## 55.28571 15.13594 17.34355
## 55.42857 15.18183 17.41372
## 55.57143 15.22722 17.48314
## 55.71429 15.27214 17.55183
## 55.85714 15.31659 17.61981
## 56.00000 15.36059 17.68711
## 56.14286 15.40415 17.75373
## 56.28571 15.44729 17.81971
## 56.42857 15.49002 17.88506
## 56.57143 15.53235 17.94980
## 56.71429 15.57429 18.01394
##
##
## $富山県
## $富山県$mean
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## [1] 2.8983 2.8983 2.8983 2.8983 2.8983 2.8983 2.8983 2.8983 2.8983 2.8983
## [11] 2.8983 2.8983 2.8983 2.8983
##
## $富山県$lower
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 -0.4584991 -2.235481
## 55.00000 -0.8686642 -2.862774
## 55.14286 -1.2383579 -3.428172
## 55.28571 -1.5776197 -3.947028
## 55.42857 -1.8929186 -4.429236
## 55.57143 -2.1887124 -4.881614
## 55.71429 -2.4682271 -5.309095
## 55.85714 -2.7338872 -5.715387
## 56.00000 -2.9875688 -6.103359
## 56.14286 -3.2307595 -6.475287
## 56.28571 -3.4646623 -6.833011
## 56.42857 -3.6902664 -7.178043
## 56.57143 -3.9083972 -7.511645
## 56.71429 -4.1197514 -7.834883
##
## $富山県$upper
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 6.255099 8.032081
## 55.00000 6.665264 8.659374
## 55.14286 7.034957 9.224772
## 55.28571 7.374219 9.743628
## 55.42857 7.689518 10.225836
## 55.57143 7.985312 10.678214
## 55.71429 8.264827 11.105695
## 55.85714 8.530487 11.511987
## 56.00000 8.784168 11.899959
## 56.14286 9.027359 12.271887
## 56.28571 9.261262 12.629611
## 56.42857 9.486866 12.974642
## 56.57143 9.704997 13.308245
## 56.71429 9.916351 13.631483
##
##
## $石川県
## $石川県$mean
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## [1] 7.758152 7.758152 7.758152 7.758152 7.758152 7.758152 7.758152 7.758152
## [9] 7.758152 7.758152 7.758152 7.758152 7.758152 7.758152
##
## $石川県$lower
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 3.694342 1.5430909
## 55.00000 3.449278 1.1682972
## 55.14286 3.217420 0.8137018
## 55.28571 2.996840 0.4763535
## 55.42857 2.786036 0.1539564
## 55.57143 2.583813 -0.1553171
## 55.71429 2.389201 -0.4529499
## 55.85714 2.201401 -0.7401653
## 56.00000 2.019744 -1.0179860
## 56.14286 1.843663 -1.2872778
## 56.28571 1.672676 -1.5487810
## 56.42857 1.506363 -1.8031347
## 56.57143 1.344361 -2.0508951
## 56.71429 1.186351 -2.2925498
##
## $石川県$upper
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 11.82196 13.97321
## 55.00000 12.06703 14.34801
## 55.14286 12.29888 14.70260
## 55.28571 12.51946 15.03995
## 55.42857 12.73027 15.36235
## 55.57143 12.93249 15.67162
## 55.71429 13.12710 15.96925
## 55.85714 13.31490 16.25647
## 56.00000 13.49656 16.53429
## 56.14286 13.67264 16.80358
## 56.28571 13.84363 17.06509
## 56.42857 14.00994 17.31944
## 56.57143 14.17194 17.56720
## 56.71429 14.32995 17.80885
##
##
## $福井県
## $福井県$mean
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## [1] 4.502667 4.584996 4.584996 4.584996 4.584996 4.584996 4.584996 4.584996
## [9] 4.584996 4.584996 4.584996 4.584996 4.584996 4.584996
##
## $福井県$lower
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 1.7591404 0.30680510
## 55.00000 1.5972175 0.01558324
## 55.14286 1.4868139 -0.15326447
## 55.28571 1.3802115 -0.31629890
## 55.42857 1.2770426 -0.47408208
## 55.57143 1.1769955 -0.62709095
## 55.71429 1.0798028 -0.77573433
## 55.85714 0.9852333 -0.92036584
## 56.00000 0.8930855 -1.06129378
## 56.14286 0.8031823 -1.19878889
## 56.28571 0.7153672 -1.33309043
## 56.42857 0.6295012 -1.46441110
## 56.57143 0.5454601 -1.59294101
## 56.71429 0.4631320 -1.71885084
##
## $福井県$upper
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 7.246194 8.698529
## 55.00000 7.572774 9.154408
## 55.14286 7.683178 9.323256
## 55.28571 7.789780 9.486291
## 55.42857 7.892949 9.644074
## 55.57143 7.992996 9.797083
## 55.71429 8.090189 9.945726
## 55.85714 8.184758 10.090358
## 56.00000 8.276906 10.231285
## 56.14286 8.366809 10.368781
## 56.28571 8.454624 10.503082
## 56.42857 8.540490 10.634403
## 56.57143 8.624532 10.762933
## 56.71429 8.706860 10.888843
##
##
## $山梨県
## $山梨県$mean
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## [1] 4.922329 6.223292 7.103737 7.699590 8.102841 8.375748 8.560441 8.685434
## [9] 8.770025 8.827273 8.866017 8.892237 8.909982 8.921991
##
## $山梨県$lower
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 1.682559 -0.03247206
## 55.00000 2.214536 0.09242837
## 55.14286 2.723156 0.40421640
## 55.28571 3.111666 0.68296509
## 55.42857 3.386924 0.89046857
## 55.57143 3.573422 1.03122434
## 55.71429 3.694841 1.11914847
## 55.85714 3.770067 1.16802906
## 56.00000 3.813097 1.18905798
## 56.14286 3.833939 1.19062709
## 56.28571 3.839604 1.17878184
## 56.42857 3.834947 1.15777971
## 56.57143 3.823303 1.13057802
## 56.71429 3.806951 1.09921318
##
## $山梨県$upper
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 8.16210 9.877131
## 55.00000 10.23205 12.354156
## 55.14286 11.48432 13.803257
## 55.28571 12.28751 14.716215
## 55.42857 12.81876 15.315214
## 55.57143 13.17807 15.720271
## 55.71429 13.42604 16.001733
## 55.85714 13.60080 16.202839
## 56.00000 13.72695 16.350992
## 56.14286 13.82061 16.463919
## 56.28571 13.89243 16.553252
## 56.42857 13.94953 16.626694
## 56.57143 13.99666 16.689386
## 56.71429 14.03703 16.744769
##
##
## $長野県
## $長野県$mean
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## [1] 16.19366 19.17694 18.68812 17.14184 16.12921 15.94520 16.19767 16.46610
## [9] 16.57639 16.55735 16.49943 16.46179 16.45512 16.46463
##
## $長野県$lower
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 9.4313936 5.8516653
## 55.00000 11.3538811 7.2126021
## 55.14286 10.3289282 5.9038402
## 55.28571 8.1615069 3.4076066
## 55.42857 6.3267129 1.1375850
## 55.57143 5.2400019 -0.4269879
## 55.71429 4.6462981 -1.4686285
## 55.85714 4.1670504 -2.3436730
## 56.00000 3.6064430 -3.2594335
## 56.14286 2.9605156 -4.2372149
## 56.28571 2.2996638 -5.2172393
## 56.42857 1.6769325 -6.1496963
## 56.57143 1.1037443 -7.0227809
## 56.71429 0.5665807 -7.8493403
##
## $長野県$upper
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 22.95593 26.53566
## 55.00000 27.00001 31.14128
## 55.14286 27.04731 31.47240
## 55.28571 26.12217 30.87607
## 55.42857 25.93170 31.12083
## 55.57143 26.65040 32.31739
## 55.71429 27.74904 33.86397
## 55.85714 28.76515 35.27587
## 56.00000 29.54634 36.41222
## 56.14286 30.15419 37.35192
## 56.28571 30.69920 38.21610
## 56.42857 31.24664 39.07327
## 56.57143 31.80649 39.93301
## 56.71429 32.36269 40.77861
##
##
## $岐阜県
## $岐阜県$mean
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## [1] 45.75286 44.53861 41.66015 36.56935 33.02017 30.01887 30.30776 31.14052
## [9] 32.07479 32.80132 33.18940 33.25634 33.10575 32.86452
##
## $岐阜県$lower
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 37.40017 32.978523
## 55.00000 35.16224 30.198688
## 55.14286 32.13410 27.091310
## 55.28571 27.01104 21.951179
## 55.42857 23.38885 18.290331
## 55.57143 20.12423 14.886324
## 55.71429 19.82560 14.276679
## 55.85714 19.46282 13.281025
## 56.00000 19.27251 12.495388
## 56.14286 19.02829 11.737285
## 56.28571 18.60558 10.885379
## 56.42857 17.98782 9.905159
## 56.57143 17.23436 8.832553
## 56.71429 16.43345 7.735367
##
## $岐阜県$upper
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 54.10555 58.52719
## 55.00000 53.91499 58.87854
## 55.14286 51.18621 56.22900
## 55.28571 46.12766 51.18752
## 55.42857 42.65150 47.75001
## 55.57143 39.91351 45.15142
## 55.71429 40.78991 46.33883
## 55.85714 42.81821 49.00001
## 56.00000 44.87708 51.65420
## 56.14286 46.57435 53.86535
## 56.28571 47.77322 55.49342
## 56.42857 48.52486 56.60752
## 56.57143 48.97714 57.37895
## 56.71429 49.29559 57.99368
##
##
## $静岡県
## $静岡県$mean
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## [1] 36.52497 48.98907 44.33332 43.46916 38.30534 37.12724 33.88792 36.69388
## [9] 39.77189 36.41228 37.57686 36.08790 37.00479 36.62583
##
## $静岡県$lower
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 25.93133 20.3233905
## 55.00000 35.91518 28.9942870
## 55.14286 30.44022 23.0856555
## 55.28571 28.80252 21.0384767
## 55.42857 22.90398 14.7509920
## 55.57143 21.02464 12.5004327
## 55.71429 17.11336 8.2334516
## 55.85714 18.01445 8.1261596
## 56.00000 19.73687 9.1309628
## 56.14286 15.42175 4.3100391
## 56.28571 15.67248 4.0769968
## 56.42857 13.30629 1.2464356
## 56.57143 13.37850 0.8714959
## 56.71429 12.18404 -0.7546689
##
## $静岡県$upper
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 47.11861 52.72654
## 55.00000 62.06296 68.98386
## 55.14286 58.22643 65.58099
## 55.28571 58.13579 65.89984
## 55.42857 53.70671 61.85970
## 55.57143 53.22984 61.75404
## 55.71429 50.66247 59.54238
## 55.85714 55.37331 65.26161
## 56.00000 59.80692 70.41282
## 56.14286 57.40280 68.51451
## 56.28571 59.48124 71.07672
## 56.42857 58.86951 70.92937
## 56.57143 60.63108 73.13808
## 56.71429 61.06763 74.00634
##
##
## $愛知県
## $愛知県$mean
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## [1] 162.4141 236.2033 253.5535 247.2705 234.6481 177.2810 101.3151 169.1799
## [9] 239.7689 255.2801 247.9467 234.7302 177.0330 100.8894
##
## $愛知県$lower
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 134.19396 119.255117
## 55.00000 200.65136 181.831297
## 55.14286 213.90359 192.914215
## 55.28571 204.89097 182.456590
## 55.42857 190.24848 166.744760
## 55.57143 131.28069 106.929599
## 55.71429 53.98844 28.935222
## 55.85714 118.12481 91.097875
## 56.00000 186.11061 157.705621
## 56.14286 199.63717 170.181574
## 56.28571 190.69971 160.394996
## 56.42857 176.13619 145.118406
## 56.57143 117.27935 85.647673
## 56.71429 40.12120 7.952441
##
## $愛知県$upper
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 190.6342 205.5731
## 55.00000 271.7552 290.5753
## 55.14286 293.2034 314.1927
## 55.28571 289.6501 312.0845
## 55.42857 279.0477 302.5514
## 55.57143 223.2813 247.6324
## 55.71429 148.6418 173.6950
## 55.85714 220.2350 247.2619
## 56.00000 293.4272 321.8322
## 56.14286 310.9230 340.3786
## 56.28571 305.1936 335.4984
## 56.42857 293.3242 324.3420
## 56.57143 236.7867 268.4183
## 56.71429 161.6577 193.8264
##
##
## $三重県
## $三重県$mean
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## [1] 25.64457 27.70668 32.12198 40.10449 34.71466 27.58482 27.24604 28.66519
## [9] 29.11460 31.26999 35.05797 32.31355 29.57373 29.32236
##
## $三重県$lower
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 20.45763 17.71183
## 55.00000 21.61910 18.39653
## 55.14286 25.73037 22.34685
## 55.28571 33.40043 29.85152
## 55.42857 27.93801 24.35067
## 55.57143 20.71882 17.08418
## 55.71429 20.29664 16.61785
## 55.85714 21.14622 17.16591
## 56.00000 21.22334 17.04597
## 56.14286 23.22807 18.97093
## 56.28571 26.86947 22.53474
## 56.42857 24.01910 19.62828
## 56.57143 21.21221 16.78589
## 56.71429 20.86882 16.39378
##
## $三重県$upper
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 30.83151 33.57731
## 55.00000 33.79427 37.01684
## 55.14286 38.51360 41.89712
## 55.28571 46.80855 50.35746
## 55.42857 41.49131 45.07865
## 55.57143 34.45082 38.08546
## 55.71429 34.19545 37.87424
## 55.85714 36.18417 40.16447
## 56.00000 37.00585 41.18323
## 56.14286 39.31192 43.56906
## 56.28571 43.24647 47.58120
## 56.42857 40.60801 44.99883
## 56.57143 37.93525 42.36157
## 56.71429 37.77590 42.25093
##
##
## $滋賀県
## $滋賀県$mean
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## [1] 30.56211 30.56211 30.56211 30.56211 30.56211 30.56211 30.56211 30.56211
## [9] 30.56211 30.56211 30.56211 30.56211 30.56211 30.56211
##
## $滋賀県$lower
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 24.53169 21.33938
## 55.00000 24.25256 20.91249
## 55.14286 23.98527 20.50370
## 55.28571 23.72842 20.11089
## 55.42857 23.48088 19.73231
## 55.57143 23.24171 19.36653
## 55.71429 23.01011 19.01233
## 55.85714 22.78541 18.66867
## 56.00000 22.56702 18.33467
## 56.14286 22.35443 18.00955
## 56.28571 22.14722 17.69264
## 56.42857 21.94498 17.38336
## 56.57143 21.74739 17.08116
## 56.71429 21.55413 16.78559
##
## $滋賀県$upper
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 36.59253 39.78484
## 55.00000 36.87165 40.21173
## 55.14286 37.13895 40.62052
## 55.28571 37.39579 41.01333
## 55.42857 37.64333 41.39190
## 55.57143 37.88250 41.75768
## 55.71429 38.11410 42.11188
## 55.85714 38.33880 42.45554
## 56.00000 38.55720 42.78954
## 56.14286 38.76978 43.11466
## 56.28571 38.97700 43.43157
## 56.42857 39.17923 43.74086
## 56.57143 39.37682 44.04305
## 56.71429 39.57009 44.33862
##
##
## $京都府
## $京都府$mean
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## [1] 105.56106 112.27672 112.12452 108.72235 111.58993 110.69013 99.93672
## [8] 109.29187 105.33559 108.42373 106.89939 104.99760 102.96673 100.06557
##
## $京都府$lower
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 94.43421 88.54401
## 55.00000 99.28956 92.41457
## 55.14286 98.51878 91.31633
## 55.28571 94.52495 87.00930
## 55.42857 96.82457 89.00826
## 55.57143 95.37786 87.27203
## 55.71429 84.09640 75.71105
## 55.85714 92.30909 83.31894
## 56.00000 87.54483 78.12697
## 56.14286 89.99431 80.23837
## 56.28571 87.85272 77.77003
## 56.42857 85.35307 74.95388
## 56.57143 82.74201 72.03568
## 56.71429 79.27683 68.27193
##
## $京都府$upper
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 116.6879 122.5781
## 55.00000 125.2639 132.1389
## 55.14286 125.7303 132.9327
## 55.28571 122.9197 130.4354
## 55.42857 126.3553 134.1716
## 55.57143 126.0024 134.1082
## 55.71429 115.7770 124.1624
## 55.85714 126.2747 135.2648
## 56.00000 123.1263 132.5442
## 56.14286 126.8531 136.6091
## 56.28571 125.9460 136.0287
## 56.42857 124.6421 135.0413
## 56.57143 123.1915 133.8978
## 56.71429 120.8543 131.8592
##
##
## $大阪府
## $大阪府$mean
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## [1] 327.9241 373.0127 395.1778 376.3714 432.1577 352.4282 285.9231 339.1307
## [9] 358.4844 371.0881 353.5661 400.0574 341.8621 278.0024
##
## $大阪府$lower
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 283.0489 259.2934
## 55.00000 316.5604 286.6764
## 55.14286 332.3488 299.0891
## 55.28571 307.7555 271.4324
## 55.42857 359.9260 321.6888
## 55.57143 276.7842 236.7407
## 55.71429 208.0032 166.7550
## 55.85714 252.4448 206.5560
## 56.00000 266.5740 217.9195
## 56.14286 275.1582 224.3759
## 56.28571 254.3448 201.8202
## 56.42857 298.2066 244.2901
## 56.57143 237.7995 182.7120
## 56.71429 172.1211 116.0709
##
## $大阪府$upper
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 372.7993 396.5548
## 55.00000 429.4650 459.3491
## 55.14286 458.0068 491.2665
## 55.28571 444.9872 481.3103
## 55.42857 504.3895 542.6267
## 55.57143 428.0723 468.1158
## 55.71429 363.8430 405.0912
## 55.85714 425.8167 471.7055
## 56.00000 450.3949 499.0494
## 56.14286 467.0181 517.8003
## 56.28571 452.7875 505.3121
## 56.42857 501.9082 555.8247
## 56.57143 445.9247 501.0121
## 56.71429 383.8838 439.9340
##
##
## $兵庫県
## $兵庫県$mean
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## [1] 138.9663 225.4863 212.8936 235.8684 213.0879 204.8233 122.1389 171.5703
## [9] 235.0413 212.5221 237.8406 210.6476 192.9455 127.1324
##
## $兵庫県$lower
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 114.51999 101.57890
## 55.00000 196.95609 181.85312
## 55.14286 182.03712 165.70270
## 55.28571 203.56953 186.47153
## 55.42857 179.83860 162.23747
## 55.57143 170.91381 152.96320
## 55.71429 87.74774 69.54215
## 55.85714 133.82575 113.84500
## 56.00000 195.76761 174.97740
## 56.14286 172.18610 150.83351
## 56.28571 196.72696 174.96270
## 56.42857 168.93659 146.85614
## 56.57143 150.75512 128.42091
## 56.71429 84.54183 61.99577
##
## $兵庫県$upper
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 163.4126 176.3537
## 55.00000 254.0165 269.1194
## 55.14286 243.7500 260.0844
## 55.28571 268.1673 285.2653
## 55.42857 246.3373 263.9384
## 55.57143 238.7328 256.6834
## 55.71429 156.5301 174.7357
## 55.85714 209.3148 229.2956
## 56.00000 274.3149 295.1051
## 56.14286 252.8581 274.2107
## 56.28571 278.9543 300.7186
## 56.42857 252.3585 274.4390
## 56.57143 235.1358 257.4700
## 56.71429 169.7229 192.2690
##
##
## $奈良県
## $奈良県$mean
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## [1] 25.98238 31.26638 31.97628 35.83944 33.84943 32.37021 29.68424 28.89109
## [9] 31.90555 32.20504 35.48300 33.74883 32.46674 30.14292
##
## $奈良県$lower
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 20.53673 17.65398
## 55.00000 25.47069 22.40263
## 55.14286 26.04009 22.89766
## 55.28571 29.79434 26.59425
## 55.42857 27.70257 24.44863
## 55.57143 26.12430 22.81791
## 55.71429 23.34102 19.98312
## 55.85714 22.27821 18.77756
## 56.00000 25.14933 21.57280
## 56.14286 25.32746 21.68668
## 56.28571 28.48978 24.78779
## 56.42857 26.64256 22.88073
## 56.57143 25.24934 21.42868
## 56.71429 22.81610 18.93751
##
## $奈良県$upper
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 31.42804 34.31079
## 55.00000 37.06207 40.13013
## 55.14286 37.91246 41.05489
## 55.28571 41.88455 45.08463
## 55.42857 39.99628 43.25023
## 55.57143 38.61612 41.92251
## 55.71429 36.02746 39.38536
## 55.85714 35.50398 39.00463
## 56.00000 38.66178 42.23831
## 56.14286 39.08262 42.72340
## 56.28571 42.47622 46.17821
## 56.42857 40.85510 44.61693
## 56.57143 39.68415 43.50481
## 56.71429 37.46974 41.34833
##
##
## $和歌山県
## $和歌山県$mean
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## [1] 12.49413 12.03931 13.79401 12.42605 13.19665 13.19665 13.19665 13.19665
## [9] 13.19665 13.19665 13.19665 13.19665 13.19665 13.19665
##
## $和歌山県$lower
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 9.810481 8.389845
## 55.00000 9.022732 7.425854
## 55.14286 10.565154 8.855899
## 55.28571 9.064102 7.284392
## 55.42857 9.570119 7.650351
## 55.57143 9.413792 7.411271
## 55.71429 9.263675 7.181686
## 55.85714 9.119080 6.960547
## 56.00000 8.979440 6.746986
## 56.14286 8.844278 6.540274
## 56.28571 8.713189 6.339790
## 56.42857 8.585825 6.145004
## 56.57143 8.461886 5.955456
## 56.71429 8.341109 5.770744
##
## $和歌山県$upper
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 15.17777 16.59841
## 55.00000 15.05588 16.65276
## 55.14286 17.02287 18.73213
## 55.28571 15.78801 17.56772
## 55.42857 16.82317 18.74294
## 55.57143 16.97950 18.98202
## 55.71429 17.12962 19.21160
## 55.85714 17.27421 19.43274
## 56.00000 17.41385 19.64630
## 56.14286 17.54901 19.85302
## 56.28571 17.68010 20.05350
## 56.42857 17.80747 20.24829
## 56.57143 17.93140 20.43783
## 56.71429 18.05218 20.62255
##
##
## $鳥取県
## $鳥取県$mean
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## [1] 2.942640 1.942088 2.462539 2.743669 2.545185 3.245798 2.369435 2.550995
## [9] 2.621541 2.648953 2.659604 2.663743 2.665351 2.665976
##
## $鳥取県$lower
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 1.3598011 0.52189659
## 55.00000 0.2102303 -0.70656002
## 55.14286 0.6934819 -0.24300049
## 55.28571 0.9599785 0.01574964
## 55.42857 0.7527937 -0.19604072
## 55.57143 1.4466026 0.49416625
## 55.71429 0.5641195 -0.39155684
## 55.85714 0.6832975 -0.30540161
## 56.00000 0.7311157 -0.26961527
## 56.14286 0.7457887 -0.26168567
## 56.28571 0.7468109 -0.26576067
## 56.42857 0.7424284 -0.27465385
## 56.57143 0.7359465 -0.28541840
## 56.71429 0.7286648 -0.29688556
##
## $鳥取県$upper
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 4.525480 5.363384
## 55.00000 3.673946 4.590737
## 55.14286 4.231597 5.168079
## 55.28571 4.527360 5.471589
## 55.42857 4.337575 5.286410
## 55.57143 5.044993 5.997429
## 55.71429 4.174751 5.130427
## 55.85714 4.418692 5.407391
## 56.00000 4.511967 5.512698
## 56.14286 4.552117 5.559592
## 56.28571 4.572397 5.584969
## 56.42857 4.585057 5.602139
## 56.57143 4.594755 5.616120
## 56.71429 4.603286 5.628837
##
##
## $島根県
## $島根県$mean
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## [1] 0.6578249 0.6578249 0.6578249 0.6578249 0.6578249 0.6578249 0.6578249
## [8] 0.6578249 0.6578249 0.6578249 0.6578249 0.6578249 0.6578249 0.6578249
##
## $島根県$lower
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 -5.545178 -8.82885
## 55.00000 -5.545178 -8.82885
## 55.14286 -5.545178 -8.82885
## 55.28571 -5.545178 -8.82885
## 55.42857 -5.545178 -8.82885
## 55.57143 -5.545178 -8.82885
## 55.71429 -5.545178 -8.82885
## 55.85714 -5.545178 -8.82885
## 56.00000 -5.545178 -8.82885
## 56.14286 -5.545178 -8.82885
## 56.28571 -5.545178 -8.82885
## 56.42857 -5.545178 -8.82885
## 56.57143 -5.545178 -8.82885
## 56.71429 -5.545178 -8.82885
##
## $島根県$upper
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 6.860828 10.1445
## 55.00000 6.860828 10.1445
## 55.14286 6.860828 10.1445
## 55.28571 6.860828 10.1445
## 55.42857 6.860828 10.1445
## 55.57143 6.860828 10.1445
## 55.71429 6.860828 10.1445
## 55.85714 6.860828 10.1445
## 56.00000 6.860828 10.1445
## 56.14286 6.860828 10.1445
## 56.28571 6.860828 10.1445
## 56.42857 6.860828 10.1445
## 56.57143 6.860828 10.1445
## 56.71429 6.860828 10.1445
##
##
## $岡山県
## $岡山県$mean
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## [1] 21.04403 23.56375 24.54727 24.20266 24.33371 23.76549 25.67451 25.99140
## [9] 26.33864 26.58729 26.76534 26.89284 26.98414 27.04952
##
## $岡山県$lower
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 12.61961 8.159988
## 55.00000 14.26942 9.349307
## 55.14286 14.71179 9.505203
## 55.28571 14.00063 8.599997
## 55.42857 13.86522 8.323537
## 55.57143 13.09106 7.440354
## 55.71429 14.83222 9.092667
## 55.85714 14.78334 8.850159
## 56.00000 14.92216 8.878644
## 56.14286 14.99472 8.857987
## 56.28571 15.01822 8.799668
## 56.42857 15.00588 8.713306
## 56.57143 14.96764 8.606488
## 56.71429 14.91083 8.484996
##
## $岡山県$upper
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 29.46846 33.92808
## 55.00000 32.85807 37.77818
## 55.14286 34.38275 39.58934
## 55.28571 34.40470 39.80533
## 55.42857 34.80219 40.34388
## 55.57143 34.43992 40.09063
## 55.71429 36.51679 42.25634
## 55.85714 37.19945 43.13264
## 56.00000 37.75512 43.79863
## 56.14286 38.17986 44.31659
## 56.28571 38.51246 44.73102
## 56.42857 38.77980 45.07237
## 56.57143 39.00064 45.36179
## 56.71429 39.18820 45.61404
##
##
## $広島県
## $広島県$mean
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## [1] 10.411595 28.099405 18.698249 14.725850 20.828425 13.219096 9.792168
## [8] 6.165601 22.178859 9.348566 14.964253 7.182023 6.109032 6.445770
##
## $広島県$lower
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 0.03880844 -5.4522132
## 55.00000 17.42648287 11.7765794
## 55.14286 7.09697498 0.9556313
## 55.28571 1.67532589 -5.2332044
## 55.42857 7.14154518 -0.1038516
## 55.57143 -1.34831491 -9.0598365
## 55.71429 -5.62056700 -13.7795759
## 55.85714 -10.42676310 -19.2102303
## 56.00000 4.76492857 -4.4534495
## 56.14286 -8.91830493 -18.5882023
## 56.28571 -4.16126076 -14.2856960
## 56.42857 -12.70913156 -23.2388727
## 56.57143 -14.54487404 -25.4783913
## 56.71429 -14.94461527 -26.2680011
##
## $広島県$upper
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 20.78438 26.27540
## 55.00000 38.77233 44.42223
## 55.14286 30.29952 36.44087
## 55.28571 27.77637 34.68490
## 55.42857 34.51531 41.76070
## 55.57143 27.78651 35.49803
## 55.71429 25.20490 33.36391
## 55.85714 22.75797 31.54143
## 56.00000 39.59279 48.81117
## 56.14286 27.61544 37.28533
## 56.28571 34.08977 44.21420
## 56.42857 27.07318 37.60292
## 56.57143 26.76294 37.69645
## 56.71429 27.83615 39.15954
##
##
## $山口県
## $山口県$mean
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## [1] 23.04669 26.03678 26.03678 26.03678 26.03678 26.03678 26.03678 26.03678
## [9] 26.03678 26.03678 26.03678 26.03678 26.03678 26.03678
##
## $山口県$lower
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 16.57564 13.15007
## 55.00000 19.11523 15.45118
## 55.14286 18.98621 15.25386
## 55.28571 18.85951 15.06010
## 55.42857 18.73501 14.86969
## 55.57143 18.61260 14.68248
## 55.71429 18.49217 14.49830
## 55.85714 18.37364 14.31702
## 56.00000 18.25691 14.13850
## 56.14286 18.14191 13.96262
## 56.28571 18.02856 13.78926
## 56.42857 17.91679 13.61833
## 56.57143 17.80654 13.44971
## 56.71429 17.69774 13.28333
##
## $山口県$upper
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 29.51775 32.94332
## 55.00000 32.95833 36.62238
## 55.14286 33.08735 36.81970
## 55.28571 33.21405 37.01346
## 55.42857 33.33855 37.20387
## 55.57143 33.46096 37.39108
## 55.71429 33.58139 37.57526
## 55.85714 33.69992 37.75654
## 56.00000 33.81665 37.93506
## 56.14286 33.93165 38.11094
## 56.28571 34.04500 38.28430
## 56.42857 34.15677 38.45523
## 56.57143 34.26702 38.62385
## 56.71429 34.37581 38.79023
##
##
## $徳島県
## $徳島県$mean
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## [1] 9.123499 11.029113 12.042240 11.688481 9.867534 7.986187 7.241144
## [8] 7.901654 9.303373 10.431870 10.623908 9.923614 8.929880 8.320945
##
## $徳島県$lower
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 6.300587 4.806228
## 55.00000 8.203305 6.707413
## 55.14286 9.200095 7.695555
## 55.28571 8.774660 7.232176
## 55.42857 6.948708 5.403575
## 55.57143 5.064876 3.518428
## 55.71429 4.285593 2.721019
## 55.85714 4.817412 3.184713
## 56.00000 6.019306 4.280825
## 56.14286 6.979289 5.151603
## 56.28571 7.082589 5.207927
## 56.42857 6.346037 4.452182
## 56.57143 5.329681 3.423851
## 56.71429 4.685090 2.760384
##
## $徳島県$upper
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 11.94641 13.44077
## 55.00000 13.85492 15.35081
## 55.14286 14.88438 16.38893
## 55.28571 14.60230 16.14479
## 55.42857 12.78636 14.33149
## 55.57143 10.90750 12.45395
## 55.71429 10.19669 11.76127
## 55.85714 10.98590 12.61860
## 56.00000 12.58744 14.32592
## 56.14286 13.88445 15.71214
## 56.28571 14.16523 16.03989
## 56.42857 13.50119 15.39505
## 56.57143 12.53008 14.43591
## 56.71429 11.95680 13.88151
##
##
## $香川県
## $香川県$mean
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## [1] 3.645687 6.467321 7.657665 8.947845 7.250516 7.033416 6.316837 7.270706
## [9] 7.739725 7.983317 7.618990 7.901256 7.697957 7.319256
##
## $香川県$lower
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 -0.142129 -2.14727770
## 55.00000 2.149557 -0.13612877
## 55.14286 3.171295 0.79635370
## 55.28571 4.391932 1.98017714
## 55.42857 2.657688 0.22639106
## 55.57143 2.416024 -0.02827575
## 55.71429 1.680099 -0.77444098
## 55.85714 2.492005 -0.03768651
## 56.00000 2.894635 0.32979900
## 56.14286 3.098513 0.51265342
## 56.28571 2.705178 0.10396371
## 56.42857 2.963167 0.34910062
## 56.57143 2.737823 0.11208744
## 56.71429 2.338189 -0.29862897
##
## $香川県$upper
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 7.433503 9.438652
## 55.00000 10.785085 13.070772
## 55.14286 12.144036 14.518977
## 55.28571 13.503759 15.915514
## 55.42857 11.843345 14.274641
## 55.57143 11.650809 14.095109
## 55.71429 10.953574 13.408114
## 55.85714 12.049408 14.579099
## 56.00000 12.584815 15.149650
## 56.14286 12.868121 15.453981
## 56.28571 12.532801 15.134016
## 56.42857 12.839346 15.453412
## 56.57143 12.658090 15.283826
## 56.71429 12.300323 14.937141
##
##
## $愛媛県
## $愛媛県$mean
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## [1] 7.190083 6.717984 6.905263 6.830970 6.860442 6.848750 6.853388 6.851548
## [9] 6.852278 6.851989 6.852104 6.852058 6.852076 6.852069
##
## $愛媛県$lower
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 3.7011558 1.8542296
## 55.00000 2.6432849 0.4862697
## 55.14286 2.0424948 -0.5316990
## 55.28571 1.3921070 -1.4870532
## 55.42857 0.8650780 -2.3086757
## 55.57143 0.3575415 -3.0786969
## 55.71429 -0.1032669 -3.7858976
## 55.85714 -0.5394831 -4.4520589
## 56.00000 -0.9496659 -5.0797657
## 56.14286 -1.3400243 -5.6766141
## 56.28571 -1.7123289 -6.2460657
## 56.42857 -2.0692235 -6.7918647
## 56.57143 -2.4123337 -7.3166159
## 56.71429 -2.7432011 -7.8226303
##
## $愛媛県$upper
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 10.67901 12.52594
## 55.00000 10.79268 12.94970
## 55.14286 11.76803 14.34222
## 55.28571 12.26983 15.14899
## 55.42857 12.85580 16.02956
## 55.57143 13.33996 16.77620
## 55.71429 13.81004 17.49267
## 55.85714 14.24258 18.15516
## 56.00000 14.65422 18.78432
## 56.14286 15.04400 19.38059
## 56.28571 15.41654 19.95027
## 56.42857 15.77334 20.49598
## 56.57143 16.11649 21.02077
## 56.71429 16.44734 21.52677
##
##
## $高知県
## $高知県$mean
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## [1] 5.054473 5.881873 4.247835 3.217112 3.385784 3.821831 2.856569 3.445002
## [9] 3.535504 3.535504 3.535504 3.535504 3.535504 3.535504
##
## $高知県$lower
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 1.6758206 -0.1127301
## 55.00000 2.1777937 0.2169726
## 55.14286 0.3450639 -1.7209383
## 55.28571 -0.8747145 -3.0407964
## 55.42857 -0.8867396 -3.1484771
## 55.57143 -0.6240520 -2.9775606
## 55.71429 -1.7561630 -4.1979960
## 55.85714 -1.5927004 -4.2594989
## 56.00000 -1.7462459 -4.5422354
## 56.14286 -1.9603187 -4.8696315
## 56.28571 -2.1663600 -5.1847444
## 56.42857 -2.3652111 -5.4888609
## 56.57143 -2.5575760 -5.7830576
## 56.71429 -2.7440508 -6.0682463
##
## $高知県$upper
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 8.433126 10.221677
## 55.00000 9.585952 11.546774
## 55.14286 8.150606 10.216608
## 55.28571 7.308938 9.475020
## 55.42857 7.658308 9.920046
## 55.57143 8.267715 10.621223
## 55.71429 7.469301 9.911134
## 55.85714 8.482704 11.149503
## 56.00000 8.817254 11.613243
## 56.14286 9.031327 11.940639
## 56.28571 9.237368 12.255752
## 56.42857 9.436219 12.559869
## 56.57143 9.628584 12.854065
## 56.71429 9.815059 13.139254
##
##
## $福岡県
## $福岡県$mean
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## [1] 173.4632 228.3930 249.7835 229.1817 213.1192 180.3145 135.9032 143.7154
## [9] 192.1162 219.5581 214.3540 211.7910 189.5052 150.4783
##
## $福岡県$lower
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 151.43469 139.77348
## 55.00000 198.31713 182.39594
## 55.14286 215.33140 197.09359
## 55.28571 192.48661 173.06141
## 55.42857 175.20679 155.13717
## 55.57143 141.47019 120.90726
## 55.71429 95.91200 74.74191
## 55.85714 99.99844 76.85608
## 56.00000 144.00090 118.53020
## 56.14286 166.95312 139.10570
## 56.28571 157.73977 127.76999
## 56.42857 151.93968 120.25633
## 56.57143 127.16296 94.16097
## 56.71429 86.16789 52.12400
##
## $福岡県$upper
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 195.4918 207.1530
## 55.00000 258.4688 274.3900
## 55.14286 284.2355 302.4733
## 55.28571 265.8767 285.3019
## 55.42857 251.0316 271.1013
## 55.57143 219.1588 239.7217
## 55.71429 175.8945 197.0646
## 55.85714 187.4324 210.5747
## 56.00000 240.2315 265.7022
## 56.14286 272.1632 300.0106
## 56.28571 270.9683 300.9381
## 56.42857 271.6422 303.3256
## 56.57143 251.8474 284.8494
## 56.71429 214.7888 248.8327
##
##
## $佐賀県
## $佐賀県$mean
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## [1] 14.59652 15.28420 14.81119 16.29537 11.52749 11.51812 15.84513 15.31451
## [9] 15.63846 16.69034 16.46492 11.46198 11.96706 14.94505
##
## $佐賀県$lower
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 11.307893 9.566997
## 55.00000 11.755372 9.887323
## 55.14286 11.029129 9.027027
## 55.28571 12.011716 9.744083
## 55.42857 6.959783 4.541782
## 55.57143 6.672004 4.106625
## 55.71429 10.693744 7.966763
## 55.85714 9.849031 6.955782
## 56.00000 9.904773 6.869542
## 56.14286 10.691924 7.516553
## 56.28571 10.208851 6.897087
## 56.42857 4.963641 1.523629
## 56.57143 5.233448 1.668891
## 56.71429 7.983445 4.298195
##
## $佐賀県$upper
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 17.88515 19.62605
## 55.00000 18.81303 20.68108
## 55.14286 18.59325 20.59535
## 55.28571 20.57903 22.84667
## 55.42857 16.09521 18.51321
## 55.57143 16.36424 18.92962
## 55.71429 20.99652 23.72350
## 55.85714 20.77999 23.67323
## 56.00000 21.37215 24.40738
## 56.14286 22.68876 25.86414
## 56.28571 22.72099 26.03276
## 56.42857 17.96031 21.40032
## 56.57143 18.70067 22.26523
## 56.71429 21.90665 25.59190
##
##
## $長崎県
## $長崎県$mean
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## [1] 21.62203 19.87884 17.93964 18.99316 12.05311 16.09231 14.12840 19.24853
## [9] 16.19026 15.10818 15.05815 12.86371 14.44935 14.17374
##
## $長崎県$lower
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 16.917292 14.4267541
## 55.00000 14.769077 12.0641320
## 55.14286 11.815986 8.5743187
## 55.28571 12.584523 9.1919945
## 55.42857 5.323523 1.7610980
## 55.57143 9.249951 5.6278255
## 55.71429 7.182693 3.5058559
## 55.85714 11.709214 7.7181425
## 56.00000 8.427302 4.3178410
## 56.14286 6.956246 2.6408698
## 56.28571 6.654203 2.2054215
## 56.42857 4.159497 -0.4482337
## 56.57143 5.499466 0.7616852
## 56.71429 4.965567 0.0910544
##
## $長崎県$upper
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 26.32677 28.81731
## 55.00000 24.98860 27.69355
## 55.14286 24.06330 27.30496
## 55.28571 25.40180 28.79433
## 55.42857 18.78269 22.34511
## 55.57143 22.93466 26.55679
## 55.71429 21.07411 24.75095
## 55.85714 26.78784 30.77891
## 56.00000 23.95322 28.06268
## 56.14286 23.26012 27.57550
## 56.28571 23.46210 27.91088
## 56.42857 21.56792 26.17565
## 56.57143 23.39923 28.13701
## 56.71429 23.38191 28.25643
##
##
## $熊本県
## $熊本県$mean
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## [1] 21.41731 27.94084 24.77744 23.65318 24.84612 24.88203 24.52672 24.60287
## [9] 24.68753 24.64478 24.63061 24.64648 24.64673 24.64206
##
## $熊本県$lower
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 13.339991 9.0641181
## 55.00000 17.813472 12.4523691
## 55.14286 13.797478 7.9850370
## 55.28571 11.474481 5.0274671
## 55.42857 11.457847 4.3705248
## 55.57143 10.522421 2.9209041
## 55.71429 9.255693 1.1716979
## 55.85714 8.437349 -0.1201625
## 56.00000 7.682986 -1.3186784
## 56.14286 6.847846 -2.5732827
## 56.28571 6.071170 -3.7536016
## 56.42857 5.353663 -4.8593366
## 56.57143 4.648700 -5.9376176
## 56.71429 3.962815 -6.9841151
##
## $熊本県$upper
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 29.49463 33.77050
## 55.00000 38.06820 43.42930
## 55.14286 35.75740 41.56985
## 55.28571 35.83188 42.27890
## 55.42857 38.23439 45.32171
## 55.57143 39.24163 46.84315
## 55.71429 39.79775 47.88175
## 55.85714 40.76840 49.32591
## 56.00000 41.69208 50.69375
## 56.14286 42.44172 51.86285
## 56.28571 43.19004 53.01481
## 56.42857 43.93930 54.15230
## 56.57143 44.64476 55.23108
## 56.71429 45.32130 56.26823
##
##
## $大分県
## $大分県$mean
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## [1] 9.829667 11.696955 12.607417 13.051345 13.267798 13.373337 13.424796
## [8] 13.449887 13.462121 13.468086 13.470994 13.472412 13.473104 13.473441
##
## $大分県$lower
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 6.317122 4.457693
## 55.00000 7.583183 5.405484
## 55.14286 8.237984 5.924946
## 55.28571 8.533950 6.142586
## 55.42857 8.643677 6.195816
## 55.57143 8.660479 6.165643
## 55.71429 8.631864 6.094640
## 55.85714 8.581463 6.004275
## 56.00000 8.520902 5.905178
## 56.14286 8.455865 5.802556
## 56.28571 8.389117 5.698934
## 56.42857 8.321990 5.595521
## 56.57143 8.255118 5.492883
## 56.71429 8.188794 5.391271
##
## $大分県$upper
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 13.34221 15.20164
## 55.00000 15.81073 17.98843
## 55.14286 16.97685 19.28989
## 55.28571 17.56874 19.96010
## 55.42857 17.89192 20.33978
## 55.57143 18.08619 20.58103
## 55.71429 18.21773 20.75495
## 55.85714 18.31831 20.89550
## 56.00000 18.40334 21.01906
## 56.14286 18.48031 21.13362
## 56.28571 18.55287 21.24305
## 56.42857 18.62283 21.34930
## 56.57143 18.69109 21.45332
## 56.71429 18.75809 21.55561
##
##
## $宮崎県
## $宮崎県$mean
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## [1] 3.397746 11.282218 6.981020 8.465205 7.645222 9.523597 8.188849
## [8] 10.030658 12.298332 9.858984 9.345231 11.263879 10.151498 10.305532
##
## $宮崎県$lower
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 -3.1667760 -6.641824
## 55.00000 2.7981673 -1.693018
## 55.14286 -2.8332883 -8.028670
## 55.28571 -2.1431541 -7.758880
## 55.42857 -3.6098143 -9.567871
## 55.57143 -2.1407298 -8.315451
## 55.71429 -3.8342156 -10.198842
## 55.85714 -2.5371221 -9.190103
## 56.00000 -0.7106901 -7.597250
## 56.14286 -3.4581095 -10.507753
## 56.28571 -4.2341514 -11.422642
## 56.42857 -2.5033499 -9.791281
## 56.57143 -3.7825897 -11.158850
## 56.71429 -3.7508941 -11.191917
##
## $宮崎県$upper
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 9.962269 13.43732
## 55.00000 19.766269 24.25745
## 55.14286 16.795329 21.99071
## 55.28571 19.073564 24.68929
## 55.42857 18.900259 24.85832
## 55.57143 21.187923 27.36264
## 55.71429 20.211914 26.57654
## 55.85714 22.598437 29.25142
## 56.00000 25.307353 32.19391
## 56.14286 23.176077 30.22572
## 56.28571 22.924614 30.11310
## 56.42857 25.031107 32.31904
## 56.57143 24.085585 31.46185
## 56.71429 24.361959 31.80298
##
##
## $鹿児島県
## $鹿児島県$mean
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## [1] 15.79103 16.95739 17.72076 18.22690 18.56885 18.80598 18.97622 19.10374
## [9] 19.20401 19.28688 19.35864 19.42331 19.48346 19.54072
##
## $鹿児島県$lower
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 9.519193 6.199081
## 55.00000 10.048634 6.391360
## 55.14286 10.494041 6.668445
## 55.28571 10.819988 6.899003
## 55.42857 11.047481 7.065910
## 55.57143 11.203887 7.179581
## 55.71429 11.311738 7.254409
## 55.85714 11.387360 7.302553
## 56.00000 11.441942 7.332950
## 56.14286 11.482935 7.351775
## 56.28571 11.515220 7.363163
## 56.42857 11.541965 7.369831
## 56.57143 11.565213 7.373546
## 56.71429 11.586276 7.375449
##
## $鹿児島県$upper
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 22.06287 25.38299
## 55.00000 23.86614 27.52342
## 55.14286 24.94748 28.77308
## 55.28571 25.63382 29.55480
## 55.42857 26.09021 30.07179
## 55.57143 26.40808 30.43238
## 55.71429 26.64069 30.69802
## 55.85714 26.82013 30.90493
## 56.00000 26.96608 31.07507
## 56.14286 27.09083 31.22199
## 56.28571 27.20206 31.35412
## 56.42857 27.30466 31.47680
## 56.57143 27.40171 31.59337
## 56.71429 27.49516 31.70599
##
##
## $沖縄県
## $沖縄県$mean
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## [1] 73.37206 71.36817 62.00038 69.40798 61.76184 62.97270 56.20580 60.84141
## [9] 60.84141 60.84141 60.84141 60.84141 60.84141 60.84141
##
## $沖縄県$lower
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 58.45448 50.55759
## 55.00000 55.16622 46.58942
## 55.14286 44.60865 35.40202
## 55.28571 50.90281 41.10676
## 55.42857 42.20652 31.85456
## 55.57143 42.42082 31.54132
## 55.71429 34.70351 23.32089
## 55.85714 37.34032 24.89960
## 56.00000 36.12277 23.03751
## 56.14286 34.96243 21.26292
## 56.28571 33.85193 19.56456
## 56.42857 32.78535 17.93337
## 56.57143 31.75786 16.36196
## 56.71429 30.76546 14.84421
##
## $沖縄県$upper
## Time Series:
## Start = c(54, 7)
## End = c(56, 6)
## Frequency = 7
## 80% 95%
## 54.85714 88.28964 96.18653
## 55.00000 87.57012 96.14692
## 55.14286 79.39211 88.59874
## 55.28571 87.91315 97.70920
## 55.42857 81.31715 91.66911
## 55.57143 83.52457 94.40407
## 55.71429 77.70810 89.09072
## 55.85714 84.34250 96.78322
## 56.00000 85.56005 98.64531
## 56.14286 86.72039 100.41990
## 56.28571 87.83089 102.11826
## 56.42857 88.89747 103.74945
## 56.57143 89.92496 105.32086
## 56.71429 90.91736 106.83861